Forest Height Estimation Research Articles

Forest Canopy Height Estimation Combining Dual-Polarization PolSAR and Spaceborne LiDAR Data

Forest canopy height data are fundamental parameters of forest structure and are critical for understanding terrestrial carbon stock, global carbon cycle dynamics and forest productivity. To address the limitations of retrieving forest canopy height using conventional PolInSAR-based methods, we proposed a method to estimate forest height by combining single-temporal polarimetric synthetic aperture radar (PolSAR) images with sparse spaceborne LiDAR (forest height) measurements. The core idea of our method is that volume scattering energy variations which are linked to forest canopy height occur during radar acquisition. Specifically, our methodology begins by employing a semi-empirical inversion model directly derived from the random volume over ground (RVoG) formulation to establish the relationship between forest canopy height, volume scattering energy and wave extinction. Subsequently, PolSAR decomposition techniques are used to extract canopy volume scattering energy. Additionally, machine learning is employed to generate a spatially continuous extinction coefficient product, utilizing sparse LiDAR samples for assistance. Finally, with the derived inversion model and the resulting model parameters (i.e., volume scattering power and extinction coefficient), forest canopy height can be estimated. The performance of the proposed forest height inversion method is illustrated with L-band NASA/JPL UAVSAR from AfriSAR data conducted over the Gabon Lope National Park and airborne LiDAR data. Compared to high-accuracy airborne LiDAR data, the obtained forest canopy height from the proposed approach exhibited higher accuracy (R2 = 0.92, RMSE = 6.09 m). The results demonstrate the potential and merit of the synergistic combination of PolSAR (volume scattering power) and sparse LiDAR (forest height) measurements for forest height estimation. Additionally, our approach achieves good performance in forest height estimation, with accuracy comparable to that of the multi-baseline PolInSAR-based inversion method (RMSE = 5.80 m), surpassing traditional PolSAR-based methods with an accuracy of 10.86 m. Given the simplicity and efficiency of the proposed method, it has the potential for large-scale forest height estimation applications when only single-temporal dual-polarization acquisitions are available.

L-Band Synthetic Aperture Radar and Its Application for Forest Parameter Estimation, 1972 to 2024: A Review.

Optical remote sensing can effectively capture 2-dimensional (2D) forest information, such as woodland area and percentage forest cover. However, accurately estimating forest vertical-structure relevant parameters such as height using optical images remains challenging, which leads to low accuracy of estimating forest stocks like biomass and carbon stocks. Thus, accurately obtaining vertical structure information of forests has become a significant bottleneck in the application of optical remote sensing to forestry. Microwave remote sensing such as synthetic aperture radar (SAR) and polarimetric SAR provides the capability to penetrate forest canopies with the L-band signal, and is particularly adept at capturing the vertical structure information of forests, which is an alternative ideal remote-sensing data source to overcome the aforementioned limitation. This paper utilizes the Citexs data analysis platform, along with the CNKI and PubMed databases, to investigate the advancements of applying L-band SAR technology to forest canopy penetration and structure-parameter estimation, and provides a comprehensive review based on 58 relevant articles from 1978 to 2024 in the PubMed database. The metrics, including annual publication numbers, countries/regions from which the publications come, institutions, and first authors, with the visualization of results, were utilized to identify development trends. The paper summarizes the state of the art and effectiveness of L-band SAR in addressing the estimation of forest height, moisture, and forest stocks, and also examines the penetration depth of the L-band in forests and highlights key influencing factors. This review identifies existing limitations and suggests research directions in the future and the potential of using L-band SAR technology for forest parameter estimation.

Error Analysis and Accuracy Improvement in Forest Canopy Height Estimation Based on GEDI L2A Product: A Case Study in the United States

Various error factors influence the inversion of forest canopy height using GEDI full-waveform LiDAR data, and the interaction of these factors impacts the accuracy of forest canopy height estimation. From an error perspective, there is still a lack of methods to fully correct the impact of various error factors on the retrieval of forest canopy height from GEDI. From the modeling perspective, establishing clear coupling models between various environments, collection parameters, and GEDI forest canopy height errors is challenging. Understanding the comprehensive impact of various environments and collection parameters on the accuracy of GEDI data is crucial for extracting high-quality and precise forest canopy heights. First, we quantitatively assessed the accuracy of GEDI L2A data in forest canopy height inversion and conducted an error analysis. A GEDI forest canopy height error correction model has been developed, taking into account both forest density and terrain effects. This study elucidated the influence of forest density and terrain on the error in forest canopy height estimation, ultimately leading to an improvement in the accuracy of forest canopy height inversion. In light of the identified error patterns, quality control criteria for GEDI footprints are formulated, and a correction model for GEDI forest canopy height is established to achieve high-precision inversion. We selected 19 forest areas located in the United States with high-accuracy Digital Terrain Models (DTMs) and Canopy Height Models (CHMs) to analyze the error factors of GEDI forest canopy heights and assess the proposed accuracy improvement for GEDI forest canopy heights. The findings reveal a decrease in the corrected RMSE value of forest canopy height from 5.60 m to 4.19 m, indicating a 25.18% improvement in accuracy.

Forest Canopy Height Retrieval Model Based on a Dual Attention Mechanism Deep Network

Accurate estimation of forest canopy height is crucial for biomass inversion, carbon storage assessment, and forestry management. However, deep learning methods are underutilized compared to machine learning. This paper introduces the convolutional neural network–bidirectional long short-term memory (CNN-BiLSTM) model and proposes a Convolutional Neural network–spatial channel attention–bidirectional long short-term memory (CNN-SCA-BiLSTM) model, incorporating dual attention mechanisms for richer feature extraction. A dataset comprising vegetation indices and canopy height data from forest regions in Luoyang, specifically within the 8–20 m range, is used for a comparative analysis of multiple models, with accuracy evaluated based on the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The results demonstrate that (1) the CNN-BiLSTM model exhibits strong potential (MAE = 1.6554 m, RMSE = 2.2393 m, R2 = 0.9115) and (2) the CNN-SCA-BiLSTM model, while slightly less efficient (<1%), demonstrates improved performance. It reduces the MAE by 0.3047 m, the RMSE by 0.6420 m, and increases the R2 value by 0.0495. Furthermore, the model is utilized to generate a canopy height map (MAE = 5.2332 m, RMSE = 7.0426 m) for Henan in the Yellow River Basin for the year 2022. The canopy height is primarily distributed around 5–20 m, approaching the accuracy levels of global maps (MAE = 4.0 m, RMSE = 6.0 m).

Long-term annual estimation of forest above ground biomass, canopy cover, and height from airborne and spaceborne sensors synergies in the Iberian Peninsula

The Mediterranean Basin has experienced substantial land use changes as traditional agriculture decreased and population migrated from rural to urban areas, which have resulted in a large forest cover increase. The combination of Landsat time series, providing spectral information, with lidar, offering three-dimensional insights, has emerged as a viable option for the large-scale cartography of forest structural attributes across large time spans. Here we develop and test a comprehensive framework to map forest above ground biomass, canopy cover and forest height in two regions spanning the most representative biomes in the peninsular Spain, Mediterranean (Madrid region) and temperate (Basque Country). As reference, we used lidar-based direct estimates of stand height and forest canopy cover. The reference biomass and volume were predicted from lidar metrics. Landsat time series predictors included annual temporal profiles of band reflectance and vegetation indices for the 1985–2023 period. Additional predictor variables including synthetic aperture radar, disturbance history, topography and forest type were also evaluated to optimize forest structural attributes retrieval. The estimates were independently validated at two temporal scales, i) the year of model calibration and ii) the year of the second lidar survey. The final models used as predictor variables only Landsat based metrics and topographic information, as the available SAR time-series were relatively short (1991–2011) and disturbance information did not decrease the estimation error. Model accuracies were higher in the Mediterranean forests when compared to the temperate forests (R2 = 0.6–0.8 vs. 0.4–0.5). Between the first (1985–1989) and the last (2020–2023) decades of the monitoring period the average forest cover increased from 21 ± 2% to 32 ± 1%, mean height increased from 6.6 ± 0.43 m to 7.9 ± 0.18 m and the mean biomass from 31.9 ± 3.6 t ha−1 to 50.4 ± 1 t ha−1 for the Mediterranean forests. In temperate forests, the average canopy cover increased from 55 ± 4% to 59 ± 3%, mean height increased from 15.8 ± 0.77 m to 17.3 ± 0.21m, while the growing stock volume increased from 137.8 ± 8.2 to 151.5 ± 3.8 m3 ha−1. Our results suggest that multispectral data can be successfully linked with lidar to provide continuous information on forest height, cover, and biomass trends.

An Advanced Terrain Vegetation Signal Detection Approach for Forest Structural Parameters Estimation Using ICESat-2 Data

Accurate forest structural parameters (such as forest height and canopy cover) support forest carbon monitoring, sustainable forest management, and the implementation of silvicultural practices. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), which is a spaceborne Light Detection and Ranging (LiDAR) satellite, offers significant potential for acquiring precise and extensive information on forest structural parameters. However, the ICESat-2 ATL08 product is significantly influenced by the geographical environment and forest characteristics, maintaining considerable potential for enhancing the accuracy of forest height estimation. Meanwhile, it does not focus on providing canopy cover data. To acquire accurate forest structural parameters, the Terrain Signal Neural Network (TSNN) framework was proposed, integrating Computer Vision (CV), Ordering Points to Identify the Clustering Structure (OPTICS), and deep learning. It encompassed an advanced approach for detecting terrain vegetation signals and constructing deep learning models for estimating forest structural parameters using ICESat-2 ATL03 raw data. First, the ATL03 footprints were visualized as Profile Raster Images of Footprints (PRIF), implementing image binarization through adaptive thresholding and median filtering denoising to detect the terrain. Second, the rough denoising buffers were created based on the terrain, combining with the OPTICS clustering and Gaussian denoising algorithms to recognize the terrain vegetation signal footprints. Finally, deep learning models (convolutional neural network (CNN), ResNet50, and EfficientNetB3) were constructed, training standardized PRIF to estimate forest structural parameters (including forest height and canopy cover). The results indicated that the TSNN achieved high accuracy in terrain detection (coefficient of determination (R2) = 0.97) and terrain vegetation signal recognition (F-score = 0.72). The EfficientNetB3 model achieved the highest accuracy in forest height estimation (R2 = 0.88, relative Root Mean Squared Error (rRMSE) = 13.5%), while the CNN model achieved the highest accuracy in canopy cover estimation (R2 = 0.80, rRMSE = 18.5%). Our results have significantly enhanced the accuracy of acquiring ICESat-2 forest structural parameters, while also proposing an original approach combining CV and deep learning for utilizing spaceborne LiDAR data.

Three-dimensional forest structure features extraction based on TomoSAR

Abstract. Forest structure is an important indicator for evaluating forest ecosystem and an indispensable input parameter for estimating forest carbon sink. Synthetic Aperture Radar (SAR) Tomography (TomoSAR), as a novel microwave remote sensing technique with 3D imaging capability, has been demonstrated to be an valuable tool for accurate inversion of forest structure. At present, the forest vertical structure parameters inversion is almost all concentrated on the forest height estimation. However, the spatial structure of forests is complex, and it is not enough to only use the forest height to express the forest structure. This fails to identify tree canopy, branch layer and underlying vegetation layer, etc., resulting in the partial and inaccurate forest vertical structure information. To solve this problem, this paper constructs 3D structural indices from tomograms, namely Horizontal Structure Index (HS) and Vertical Structure Index (VS). The HS primarily describes canopy density, compensating for the lack of information on the horizontal dimension of forest structure provided by traditional TomoSAR techniques; while VS describes the complexity of canopy distribution in the altitude direction, further enriching information on forest structure vertically. In order to verify their feasibility and validity, HS and VS are extracted using six fully-polarized L-band SAR images covering the Krycklan watershed in Sweden. Moreover, the accuracy is verified by using external high-precision LiDAR data. The results indicate a strong correlation between the structural indices extracted from TomoSAR and those extracted from LiDAR.

Uncertainty analysis for forest height inversion using L / P band PolInSAR datasets and RVoG model over kryclan forest site

Forest height, as a measure of the quantity and quality of forest resources, plays a significant role in the study of the ecological functions performed by forests. Although the polarimetric synthetic aperture radar interferometry (PolInSAR) technique has evolved as a potent method for forest height inversion, uncertainties still exist in the process of estimating forest height, and the uncertainties in predicted forest height directly lead into the uncertainty of terrestrial carbon stock calculation results. In this study, we took the Random Volume over Ground (RVoG) model as likelihood function and constructed a hierarchical Bayesian framework to calculate and reduce the uncertainty of forest height inversion using L / P band PolInSAR airborne data via RVoG model. Uncertainties resulted from five canopy types and three forest densities were analyzed, respectively. The results showed that among the five different canopy types, L band has the highest prediction accuracy in pure coniferous canopy with Acc. = 0.90. The uncertainty is extremely low for pure forest, with the ratio of uncertainty values of 0.09 for L band and 0.15 for the P band in pure coniferous canopy, and uncertainty values of 0.16 for L band and 0.11 for P band in pure broadleaf canopy, respectively. Furthermore, when the forest density is between 300 and 600 stems/ha, the ratio of uncertainties for the L band is 0.27, whereas the P band is 0.24. As forest density increases, the uncertainty in forest height estimates decreases for both bands. The changes in canopy types and forest density affect forest height estimation uncertainties obviously, the effects are different at each frequency. The forest height inversion accuracy of the L band in pure coniferous canopy surpasses that in other canopy types, with the lowest uncertainty. P band performed well in broadleaf canopy forest height inversion. The inversion uncertainties at both frequencies decrease with increase of forest densities.

Estimation of canopy height based on multi-source remote sensing data using forest structure aided sample selection

ABSTRACT Forest canopy height data are crucial for estimating forest carbon storage and assessing forest ecology. By utilizing satellite imagery, canopy height data obtained from airborne or spaceborne LiDAR have been expanded from footprint and plot levels to spatially continuous elevation mapping of forests. However, current research suggests that estimating forest canopy height without forest type data presents a challenge in how to effectively integrate multi-source LiDAR data and ensure the samples adequately represent various forest types for higher estimation accuracy. Therefore, this study proposes a forest canopy height estimation method that considers forest structure and integrates multi-source LiDAR data to overcome the challenge. First, a stratified sampling method based on forest structure (SSMFS) was proposed to select training samples and enhance their representativeness. Second, we combined GEDI and ATL08 data to create a multi-source spaceborne LiDAR dataset, enhancing geographic coverage and increasing canopy height samples. Third, the spaceborne LiDAR-based canopy height estimation model incorporates previously unconsidered canopy openness features and uses SSMFS to select training samples. Finally, we improved spaceborne LiDAR canopy height accuracy by creating a residual correction model that adjusts for differences between airborne scanner (ALS) and spaceborne LiDAR estimates. This study, conducted in Zhangwu County, achieved an accuracy of R2 = 0.71, MAE = 1.20 m, and RMSE = 1.71 m. These results show a 51.06% increase in R2, a 26.38% decrease in MAE, and a 24.00% decrease in RMSE compared to recent research. In summary, this study profoundly amplifies predictive accuracy, providing a clear advantage in the delineation of regional forest canopy maps.

High-density forest AGB estimation in tropical forest integrated with PolInSAR multidimensional features and optimized machine learning algorithms

Accurately estimating the above-ground biomass (AGB) of high-density tropical rainforests is a challenging issue. In this study, airborne multi-baseline PolInSAR data were used to estimate the tropical rainforest AGB in Gabon, Africa. The most suitable baseline combination of the PolInSAR data was initially determined through baseline selection, and the PolInSAR parameters related to forest height were obtained based on the forest canopy height estimation theory and microwave penetration depth theory. The height parameter, baseline parameter, and observation geometry parameter were then used as independent variables to construct the AGB regression model. Support vector regression (SVR) was chosen as the AGB estimation model, and the global best particle swarm algorithm (GLB-PSO) was used to optimize the SVR model’s parameters. The results show that the RFECV variable selection method is superior to the Pearson method. The GLB-PSO algorithm can also further improve the saturation point of the SVR model—the estimation results show that the saturation point of AGB estimation of PolInSAR multidimensional features combined with the SVR machine learning algorithm is up to 500 Mg/ha, while this saturation point can be increased to 650 Mg/ha when using the GLB-PSO-SVR algorithm.

Forest Canopy Height Estimation by Integrating Structural Equation Modeling and Multiple Weighted Regression

As an important component of forest parameters, forest canopy height is of great significance to the study of forest carbon stocks and carbon cycle status. There is an increasing interest in obtaining large-scale forest canopy height quickly and accurately. Therefore, many studies have aimed to address this issue by proposing machine learning models that accurately invert forest canopy height. However, most of the these approaches feature PolSAR observations from a data-driven viewpoint in the feature selection part of the machine learning model, without taking into account the intrinsic mechanisms of PolSAR polarization observation variables. In this work, we evaluated the correlations between eight polarization observation variables, namely, T11, T22, T33, total backscattered power (SPAN), radar vegetation index (RVI), the surface scattering component (Ps), dihedral angle scattering component (Pd), and body scattering component (Pv) of Freeman-Durden three-component decomposition, and the height of the forest canopy. On this basis, a weighted inversion method for determining forest canopy height under the view of structural equation modeling was proposed. In this study, the direct and indirect contributions of the above eight polarization observation variables to the forest canopy height inversion task were estimated based on structural equation modeling. Among them, the indirect contributions were generated by the interactions between the variables and ultimately had an impact on the forest canopy height inversion. In this study, the covariance matrix between polarization variables and forest canopy height was calculated based on structural equation modeling, the weights of the variables were calculated by combining with the Mahalanobis distance, and the weighted inversion of forest canopy height was carried out using PSO-SVR. In this study, some experiments were carried out using three Gaofen-3 satellite (GF-3) images and ICESat-2 forest canopy height data for some forest areas of Gaofeng Ridge, Baisha Lizu Autonomous County, Hainan Province, China. The results showed that T11, T33, and total backscattered power (SPAN) are highly correlated with forest canopy height. In addition, this study showed that determining the weights of different polarization observation variables contributes positively to the accurate estimation of forest canopy height. The forest canopy height-weighted inversion method proposed in this paper was shown to be superior to the multiple regression model, with a 26% improvement in r and a 0.88 m reduction in the root-mean-square error (RMSE).

Simultaneous Estimation of Subcanopy Topography and Forest Height With Single-Baseline Single-Polarization TanDEM-X Interferometric Data Combined With ICESat-2 Data

Simultaneous Estimation of Subcanopy Topography and Forest Height With Single-Baseline Single-Polarization TanDEM-X Interferometric Data Combined With ICESat-2 Data

A Fourier–Legendre Polynomial Forest Height Inversion Model Based on a Single-Baseline Configuration

In this article, we propose a Fourier–Legendre (FL) polynomial forest height estimation algorithm based on low-frequency single-baseline polarimetric interferometric synthetic aperture radar (PolInSAR) data. The algorithm can obtain forest height with a single-baseline PolInSAR configuration while capturing a high-resolution vertical profile for the forest volume. This is based on the consideration that the forest height remains constant within neighboring pixels. Meanwhile, we also assume that the coefficients of the FL polynomials remain unchanged within neighboring pixels, except for the last polynomial coefficient. The idea of using neighboring pixels to increase the observations provides us with the possibility to obtain high-order FL polynomials. With this approach, it is possible to obtain a high-resolution vertical profile that is suitable for forest height estimation without losing too much spatial resolution. P-band PolInSAR data acquired in Mabounie in Gabon and Krycklan in Sweden were selected for testing the proposed algorithm. The results show that the algorithm outperforms the random volume over ground (RVoG) model by 18% and 16.7% in forest height estimation for the Mabounie and Krycklan study sites, respectively.

Systematic Evaluation of Multi-Resolution ICESat-2 Canopy Height Data: A Case Study of the Taranaki Region

Forest canopy height data are essential to assessing forest biomass and carbon storage, and they can be efficiently retrieved using the Advanced Terrain Laser Altimetry System (ATLAS) onboard ICESat-2. However, due to the sparse and discrete distribution of ATLAS footprints, the wall-to-wall mapping of forest canopy heights requires a combination of other ancillary data. In order to match the ATLAS data with ancillary data, or estimate higher-resolution canopy heights, the standard ATLAS canopy height products (ATL08), reported at a fixed step size of 100 m (12 m × 100 m), are typically divided into 30 m step sizes. There is little concern about the accuracy of the generated 30 m segment (12 m × 30 m) dataset. Furthermore, previous studies have primarily evaluated the along-track performance of the canopy height product, rather than the ability of ATLAS to provide an accurate representation of canopy heights at the pixel-wise level. In this study, we use airborne LiDAR data as references with which to evaluate the along-track accuracy of 30 m segment canopy height products and their accuracy in representing canopy height at the pixel-wise level. A framework method for spatially matching ATLAS canopy height estimate data with ancillary data accurately is also established in this study. This enables the use of ATLAS data to characterize pixel-wise canopy heights more precisely. The results show that the accuracy of the 30 m segment version dataset in representing the pixel-wise canopy heights (R2 = 0.38, RMSE = 8.37 m) is lower than its along-track accuracy (R2 = 0.44, RMSE = 7.63 m). Using only the nighttime data for the Taranaki region, the proposed method improves the estimation of pixel-wise forest canopy heights, with the R2 increasing from 0.49 to 0.59, the RMSE decreasing from 7.48 m to 5.51 m, and the %RMSE decreasing from 36.7% to 27.6%. This study contributes to understanding the accuracy of the ATLAS in reflecting pixel-wise canopy height and provides a new way of spatially matching ATLAS canopy height data with other remote sensing data.

Assessing the performance of random forest regression for estimating canopy height in tropical dry forests

<span lang="EN-US">Accurate estimation of forest canopy height is essential for monitoring forest ecosystems and assessing their carbon storage potential. This study evaluates the effectiveness of different remote sensing techniques for estimating forest canopy height in tropical dry forests. Using field data and remote sensing data from airborne lidar and polarimetric synthetic aperture radar (SAR), a random forest (RF) model was developed to estimate canopy height based on different indices. Results show that the normalize difference build-up index (NDBI) has the highest correlation with canopy height, outperforming other indices such as relative vigor index (RVI) and polarimetric vertical and horizontal variables. The RF model with NDBI as input showed a good fit and predictive ability, with low concentration of errors around 0. These findings suggest that NDBI can be a useful tool for accurately estimating forest canopy height in tropical dry forests using remote sensing techniques, providing valuable information for forest management and conservation efforts.</span>

Forest Height Inversion by Combining Single-Baseline TanDEM-X InSAR Data with External DTM Data

Forest canopy height estimation is essential for forest management and biomass estimation. In this study, we aimed to evaluate the capacity of TanDEM-X interferometric synthetic aperture radar (InSAR) data to estimate canopy height with the assistance of an external digital terrain model (DTM). A ground-to-volume ratio estimation model was proposed so that the canopy height could be precisely estimated from the random-volume-over-ground (RVoG) model. We also refined the RVoG inversion process with the relationship between the estimated penetration depth (PD) and the phase center height (PCH). The proposed method was tested by TanDEM-X InSAR data acquired over relatively homogenous coniferous forests (Teruel test site) and coniferous as well as broadleaved forests (La Rioja test site) in Spain. Comparing the TanDEM-X-derived height with the LiDAR-derived height at plots of size 50 m × 50 m, the root-mean-square error (RMSE) was 1.71 m (R2 = 0.88) in coniferous forests of Teruel and 1.97 m (R2 = 0.90) in La Rioja. To demonstrate the advantage of the proposed method, existing methods based on ignoring ground scattering contribution, fixing extinction, and assisting with simulated spaceborne LiDAR data were compared. The impacts of penetration and terrain slope on the RVoG inversion were also evaluated. The results show that when a DTM is available, the proposed method has the optimal performance on forest height estimation.

A novel coherence optimization algorithm for forest height inversion using single-baseline PolInSAR images

Forest height is one of the influential information for the management of forest cover and is also one of the criteria to evaluate the growth of organisms in the forest ecosystem. This paper suggests a novel coherence optimization algorithm to increase the accuracy of forest height estimation using the L-band PolInSAR images. First, the ground phase and coherence line are determined based on the mean of the coherence set. Then, the proposed algorithm is developed by adding more forced conditions to determine the volume-only coefficient coherence optimization. Finally, the forest height and extinction coefficient can be extracted by comparing the volume-only optimal coherence coefficient with the values in the look-up table. The effectiveness of the proposed method was evaluated with PolInSAR simulated and spaceborne data. Experimental results show that the proposed method has improved the accuracy of estimated forest height by more than 0.9m compared with the forest height retrieval algorithm by Tayebe.

Forest terrain and canopy height estimation using stereo images and spaceborne LiDAR data from GF-7 satellite

ABSTRACT Accurate estimation of forest terrain and canopy height is crucial for timely understanding of forest growth. Gao Fen-7 (GF-7) Satellite is China’s first sub-meter-level three-dimensional (3D) mapping satellite for civilian use, which was equipped with a two-line-array stereo mapping camera and a laser altimeter system that can provide stereo images and full waveform LiDAR data simultaneously. Most of the existing studies have concentrated on evaluating the accuracy of GF-7 for topographic survey in bare land, but few have in-depth studied its ability to measure forest terrain elevation and canopy height. The purpose of this study is to evaluate the potential of GF-7 LiDAR and stereo image for forest terrain and height measurement. The Airborne Laser Scanning (ALS) data were utilized to generate reference terrain and forest vertical information. The validation test was conducted in Pu’er City, Yunnan Province of China, and encouraging results have obtained. The GF-7 LiDAR data obtained the accuracy of forest terrain elevation with RMSE of 8.01 m when 21 available laser footprints were used for results verification; meanwhile, when it was used to calculate the forest height, R2 of 0.84 and RMSE of 3.2 m were obtained although only seven effective footprints were used for result verification. The canopy height values obtained from GF-7 stereo images have also been proven to have high accuracy with the resolution of 20 m × 20 m compared with ALS data (R2 = 0.88, RMSE = 2.98 m). When the results were verified at the forest sub-compartment scale that taking into account the forest types, further higher accuracy (R2 = 0.96, RMSE = 1.23 m) was obtained. These results show that GF-7 has considerable application potential in forest resources monitoring.

Improving Forest Canopy Height Estimation Using a Semi-Empirical Approach to Overcome TomoSAR Phase Errors

Forest canopy height is an important forest indicator parameter. Synthetic aperture radar tomography (TomoSAR) is an effective method to characterize forest canopy height and describe forest 3D structure; however, the residual phase error of TomoSAR affects the focus of the relative reflectance and can lead to errors in forest canopy height estimation. Therefore, this paper proposes a semi-empirical method to overcome the residual phase effects on forest canopy height estimation. In this study, we used airborne multi-baseline UAVSAR data to estimate forest canopy height via TomoSAR techniques and applied a semi-empirical method to improve forest canopy height estimation without phase calibration to mitigate the effects of phase error. The process is divided into three stages: the first step uses a semi-empirical method to initially determine the optimal relative reflectance loss threshold (K) by excluding the inverse extremes; in the second and third steps, the percentile height was used to gradually reduce the height interval between the upper and lower envelopes to minimize overestimation of extreme values and the lower vegetation. When the root mean square error (RMSE) was minimized, the percentile combinations were determined between the inversion results and a LiDAR dataset of the area. The results show that the canopy height estimation results are not satisfactory when relying solely on the K value to estimate the height difference between the envelope at the top of the forest and the ground; the best result was obtained when K = 0.4, but the corresponding R2 value was only 0.13, and the RMSE was 15.23 m. In our proposed method, the K value is determined as 0.3 by excluding the extreme values of the inversion result in the initial step—the corresponding R2 and RMSE values were 0.59 and 10.73 m, respectively, representing an RMSE decrease of 29.54% relative to the initial K value. After two steps of correction overestimation, the inversion accuracy was significantly improved with an R2 value of 0.65 and an RMSE of 9.69 m, corresponding to an RMSE decrease of 36.38%. Overall, the findings of the study represent an important reference for optimizing future spaceborne TomoSAR forest canopy height estimates.

Fine-Resolution Forest Height Estimation by Integrating ICESat-2 and Landsat 8 OLI Data with a Spatial Downscaling Method for Aboveground Biomass Quantification

Rapid and accurate estimation of forest aboveground biomass (AGB) with fine details is crucial for effective forest monitoring and management, where forest height plays a key role in AGB quantification. In this study, we propose a random forest (RF)-based down-scaling method to map forest height and biomass at a 15-m resolution by integrating Landsat 8 OLI and Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) LiDAR data. ICESat-2 photon data are used to derive canopy parameters along 15-m segments, which are considered sample plots for the extrapolation of discrete forest height. Fourteen variables associated with spectral features, textual features and vegetation index are extracted from pan-sharpened Landsat 8 images. A regression function is established between these variables and ICESat-2-derived forest height to produce a 15-m continuous forest height distribution data based on the 30-m forest height product using the RF algorithm. Finally, a wall-to-wall forest AGB at 15-m spatial resolution is achieved by using an allometric model specific to the forest type and height. The Jilin Province in northeast China is taken as the study area, and the forest AGB estimation results reveal a density of 61.15 Mg/ha with a standard deviation of 89.46 Mg/ha. The R2 between our predicted forest heights and the ICESat-2-derived heights reaches 0.93. Validation results at the county scale demonstrate reasonable correspondence between the estimated AGB and reference data, with consistently high R2 value exceeding 0.65. This downscaling method provides a promising scheme to estimate spatial forest AGB with fine details and to enhance the accuracy of AGB estimation, which may facilitate carbon stock measurement and carbon cycle studies.