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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.

Developing countries are required to produce robust estimates of forest carbon stocks for successful implementation of climate change mitigation policies related to reducing emissions from deforestation and degradation (REDD). Here we present a \"benchmark\" map of biomass carbon stocks over 2.5 billion ha of forests on three continents, encompassing all tropical forests, for the early 2000s, which will be invaluable for REDD assessments at both project and national scales. We mapped the total carbon stock in live biomass (above- and belowground), using a combination of data from 4,079 in situ inventory plots and satellite light detection and ranging (Lidar) samples of forest structure to estimate carbon storage, plus optical and microwave imagery (1-km resolution) to extrapolate over the landscape. The total biomass carbon stock of forests in the study region is estimated to be 247 Gt C, with 193 Gt C stored aboveground and 54 Gt C stored belowground in roots. Forests in Latin America, sub-Saharan Africa, and Southeast Asia accounted for 49%, 25%, and 26% of the total stock, respectively. By analyzing the errors propagated through the estimation process, uncertainty at the pixel level (100 ha) ranged from ±6% to ±53%, but was constrained at the typical project (10,000 ha) and national (>1,000,000 ha) scales at ca. ±5% and ca. ±1%, respectively. The benchmark map illustrates regional patterns and provides methodologically comparable estimates of carbon stocks for 75 developing countries where previous assessments were either poor or incomplete.

Forest aboveground biomass (AGB) is a key measurement in studying terrestrial carbon storage, carbon cycle, and climate change. Machine learning based algorithms can be applied to estimate forest AGB using remote sensing-based data. Our study utilized L-band ALOS-2/PALSAR-2 Synthetic Aperture Radar (SAR) data in combination with multi-parameter linear regression (LR) and Random forest regression (RF) for forest carbon estimation. Six L-band fully polarimetric acquisitions are used in this study. The input parameters to the RF algorithm are the backscatter, decomposition powers and species information. The multi-temporal backscatter (HH1 to HH6, HV1 to HV6, VV1 to VV6) and the temporal average are used. Furthermore, average decomposi-tion parameters from G4U decomposition—Double bounce (Dbl), Odd bounce (Odd), Volume scattering (Vol), and Helix scattering (Hlx) for all six dates. In the first case (1), the model is trained to estimate only the AGB. In the second case (2), the model is trained for forest height estimation. In the third case (3), the model is trained to predict both the AGB and height of the forest. In contrast to the LR method, there is a significant improvement in AGB estimation achieved with the RF algorithms. This study shows the potential of combined retrieval of AGB and forest height using time-series L-band backscatter data.

The U.S. Forest Service, Forest Inventory and Analysis (FIA) program is tasked with making and reporting estimates of various forest attributes using a design-based network of permanent sampling plots. To make its estimates more precise, FIA uses a technique known as post-stratification to group plots into more homogenous classes, which helps lower variance when deriving population means. Currently FIA uses a nationally available map of tree canopy cover for post-stratification, which tends to work well for forest area estimates but less so for structural attributes like volume. Here we explore the use of new statewide digital aerial photogrammetric (DAP) point clouds developed from stereo imagery collected by the National Agricultural Imagery Program (NAIP) to improve these estimates in the southeastern mixed hardwood forests of Tennessee and Virginia, United States (U.S.). Our objectives are to 1. evaluate the relative quality of NAIP DAP point clouds using airborne LiDAR and FIA tree height measurements, and 2. assess the ability of NAIP digital height models (DHMs) to improve operational forest inventory estimates above the gains already achieved from FIA’s current post-stratification approach. Our results show the NAIP point clouds were moderately to strongly correlated with FIA field measured maximum tree heights (average Pearson’s r = 0.74) with a slight negative bias (−1.56 m) and an RMSE error of ~4.0 m. The NAIP point cloud heights were also more accurate for softwoods (R2s = 0.60–0.79) than hardwoods (R2s = 0.33–0.50) with an error structure that was consistent across multiple years of FIA measurements. Several factors served to degrade the relationship between the NAIP point clouds and FIA data, including a lack of 3D points in areas of advanced hardwood senescence, spurious height values in deep shadows and imprecision of FIA plot locations (which were estimated to be off the true locations by +/− 8 m). Using NAIP strata maps for post-stratification yielded forest volume estimates that were 31% more precise on average than estimates stratified with tree canopy cover data. Combining NAIP DHMs with forest type information from national map products helped improve stratification performance, especially for softwoods. The monetary value of using NAIP height maps to post-stratify FIA survey unit total volume estimates was USD 1.8 million vs. the costs of installing more field plots to achieve similar precision gains. Overall, our results show the benefit and growing feasibility of using NAIP point clouds to improve FIA’s operational forest inventory estimates.

Accurate estimation of forest height is crucial for the estimation of forest aboveground biomass and monitoring of forest resources. Remote sensing technology makes it achievable to produce high-resolution forest height maps in large geographical areas. In this study, we produced a 25 m spatial resolution wall-to-wall forest height map in Baoding city, north China. We evaluated the effects of three factors on forest height estimation utilizing four types of remote sensing data (Sentinel-1, Sentinel-2, ALOS PALSAR-2, and SRTM DEM) with the National Forest Resources Continuous Inventory (NFCI) data, three feature selection methods (stepwise regression analysis (SR), recursive feature elimination (RFE), and Boruta), and six machine learning algorithms (k-nearest neighbor (k-NN), support vector machine regression (SVR), random forest (RF), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and categorical boosting (CatBoost)). ANOVA was adopted to quantify the effects of three factors, including data source, feature selection method, and modeling algorithm, on forest height estimation. The results showed that all three factors had a significant influence. The combination of multiple sensor data improved the estimation accuracy. Boruta’s overall performance was better than SR and RFE, and XGBoost outperformed the other five machine learning algorithms. The variables selected based on Boruta, including Sentinel-1, Sentinel-2, and topography metrics, combined with the XGBoost algorithm, provided the optimal model (R2 = 0.67, RMSE = 2.2 m). Then, we applied the best model to create the forest height map. There were several discrepancies between the generated forest height map and the existing map product, and the values with large differences between the two maps were mostly distributed in the steep areas with high slope values. Overall, we proposed a methodological framework for quantifying the importance of data source, feature selection method, and machine learning algorithm in forest height estimation, and it was proved to be effective in estimating forest height by using freely accessible multi-source data, advanced feature selection method, and machine learning algorithm.

The assessment of forest structural parameters is crucial for understanding carbon storage, habitat suitability, and timber stock. However, the labor-intensive and expensive nature of field measurements, coupled with inadequate sample sizes for large-scale modeling, poses challenges. To address the forest structure parameters in the Western Tianshan Mountains, this study used UAV-LiDAR to gather extensive sample data. This approach was enhanced by integrating Sentinel satellite and topographic data and using a Bayesian-Random Forest model to estimate forest canopy height, average height, density, and aboveground biomass (AGB). Validation against independent LiDAR-derived samples confirmed the model’s high accuracy, with coefficients of determination (R2) and root mean square errors (RMSE) indicating strong predictive performance (R2 = 0.63, RMSE = 5.06 m for canopy height; R2 = 0.64, RMSE = 2.88 m for average height; R2 = 0.68, RMSE = 62.84 for density; and R2 = 0.59, RMSE = 29.71 Mg/ha for AGB). Notably, the crucial factors include DEM, Sentinel-1 (VH and VV backscatter in dB), and Sentinel-2 (B6, B8A, and B11 bands). These factors contribute significantly to the modeling of forest structure. This technology aims to expedite and economize forest surveys while augmenting the range of forest parameters, especially in remote and rugged terrains. Using a wealth of UAV-LiDAR data, this outcome surpasses its counterparts’ by providing essential insights for exploring climate change effects on Central Asian forests, facilitating precise carbon stock quantification, and enhancing knowledge of forest ecosystems.

Forest stand age is a meaningful metric, which reflects the past disturbance legacy, provides guidelines for forest management practices, and is an important factor in qualifying forest carbon cycles and carbon sequestration potential. Reliable large‐scale forest stand age information with high spatial resolutions, however, is difficult to obtain. In this study, we developed a top‐down method to downscale the provincial statistics of national forest inventory data into 1 km stand age map using climate data and light detection and ranging‐derived forest height. We find that the distribution of forest stand age in China is highly heterogeneous across the country, with a mean value of ~42.6 years old. The relatively young stand age for Chinese forests is mostly due to the large proportion of newly planted forests (0–40 years old), which are more prevailing in south China. Older forests (stand age > 60 years old) are more frequently found in east Qinghai‐Tibetan Plateau and the central mountain areas of west and northeast China, where human activities are less intensive. Among the 15 forest types, forests dominated by species of Taxodiaceae, with the exception of Cunninghamia lanceolata stands, have the oldest mean stand age (136 years), whereas Pinus massoniana forests are the youngest (18 years). We further identified uncertainties associated with our forest age map, which are high in west and northeast China. Our work documents the distribution of forest stand age in China at a high resolution which is useful for carbon cycle modeling and the sustainable use of China's forest resources. Key Points The mean forest age in China is ~43 years, with a large spatial heterogeneity resulting from human and natural disturbances Forests in south China are generally young, while older forests are mostly found in east Qinghai‐Tibetan Plateau and west and northeast China Forest types and climate factors are major factors determining forest growth rate

Forests absorb atmospheric carbon and hence play a vital role in carbon sequestration and climate regulation. Recent research emphasizes developing technology and methods to understand the carbon sequestration potential in various forest ecosystems. Forest stand height estimation is one of the crucial parameters in allometry that estimates forest biomass. An attempt is made in this study to map forest stand height in tropical and sub-tropical forests in India using recently launched spaceborne LiDAR platforms Ice Cloud and Elevation Satellite (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI). A geostatistical kriging approach is used to interpolate the forest stand height, and the generated stand height surface is validated using ground truth samples. The results showed that GEDI data performed better with an RMSE of 3.99 m and 2.62 m in tropical forests than the ICESat-2 data, which showed an RMSE of 5.71 m and 5.08 m, respectively. A similar pattern was observed in sub-tropical forests where GEDI modelled stand height outperformed ICESat-2 modelled stand height. This analysis demonstrates the potential of existing spaceborne LiDAR platforms in interpolating forest stand height at different forest types. Also, the research emphasizes the necessity of a high density of LiDAR footprints spread in both across- and along-track directions for accurate interpolation of forest stand height.

Interferometric synthetic aperture radar (InSAR) technology can be used to produce a high spatial resolution digital elevation model (DEM) on a global scale. However, above-ground vegetation strongly biases the DEM accuracy in forested areas by shifting the scattering phase center below the top of the canopy and above the underlying topography. Such a bias is related to the radar wavelength, forest structure, dielectric property, radar incidence angle, and terrain slope. In this paper, based on the strong penetration characteristics of the low-frequency (P-band) SAR signal, the time–frequency (TF) analysis is introduced to increase the InSAR observation space, where the decomposed sublook images can also be utilized to perform interferometry. By analyzing and modeling the sublook interferograms, a comprehensive dual-baseline framework for correcting the InSAR phase shifting is proposed to generate accurate digital terrain, surface, and canopy height models (DTM, DSM, and CHM) over forest areas. The proposed method is validated using P-band InSAR datasets acquired above the boreal coniferous forest in Remningstorp, southern Sweden, and the rainforest in Lopé, Gabon, Africa. For the boreal forest, the root-mean-square errors (RMSEs) in terms of the DTM, CHM, and DSM range from 2 to 4 m, while for the tropical rainforest with complicated topography, the RMSEs of the three elevation models range from 5 to 8 m compared with light detection and ranging (LiDAR) references. The high consistency between the InSAR-derived DTM/DSM and references demonstrates the effectiveness and stability of the proposed method and represents an improvement of 49–70% compared to the raw InSAR DEM.

As an important parameter of forest biomass, forest height is of great significance for the calculation of forest carbon stock and the study of the carbon cycle in large-scale regions. The main idea of the current forest height inversion methods using multi-baseline P-band polarimetric interferometric synthetic aperture radar (PolInSAR) data is to select the best baseline for forest height inversion. However, the approach of selecting the optimal baseline for forest height inversion results in the process of forest height inversion being unable to fully utilize the abundant observation data. In this paper, to solve the problem, we propose a multi-baseline forest height inversion method combining analytic and geometric expression of the random volume over ground (RVoG) model, which takes into account the advantages of the selection of the optimal observation baseline and the utilization of multi-baseline information. In this approach, for any related pixel, an optimal baseline is selected according to the geometric structure of the coherence region shape and the functional model for forest height inversion is established by the RVoG model’s analytic expression. In this way, the other baseline observations are transformed into a constraint condition according to the RVoG model’s geometric expression and are also involved in the forest height inversion. PolInSAR data were used to validate the proposed multi-baseline forest height inversion method. The results show that the accuracy of the forest height inversion with the algorithm proposed in this paper in a coniferous forest area and tropical rainforest area was improved by 17% and 39%, respectively. The method proposed in this paper provides a multi-baseline PolInSAR forest height inversion scheme for exploring regional high-precision forest height distribution. The scheme is an applicable method for large-scale, high-precision forest height inversion tasks.