Forest Canopy Height Research Articles

Predicting and Mapping Dominant Height of Oriental Beech Stands Using Environmental Variables in Sinop, Northern Turkey

The dominant height of forest stands (SDH) is an essential indicator of site productivity in operational forest management. It refers to the capacity of a particular site to support stand growth. Sites with taller dominant trees are typically more productive and may be more suitable for certain management practices. The present study investigated the relationship between the dominant height of oriental beech stands and numerous environmental variables, including physiographic, climatic, and edaphic attributes. We developed models and generated maps of SDH using multilinear regression (MLR) and regression tree (RT) techniques based on environmental variables. With this aim, the total height, diameter at breast height, and age of sample trees were measured on 222 sample plots. Additionally, topsoil samples (0–20 cm) were collected from each plot to analyze the physical and chemical soil properties. The statistical results showed that latitude, elevation, mean annual maximum temperature, and several soil attributes (i.e., bulk density, field capacity, organic carbon, and pH) were significantly correlated with the SDH. The RT model outperformed the MLR model, explaining 57% of the variation in the SDH with an RMSE of 2.37 m. The maps generated by both models clearly indicated an increasing trend in the SDH from north to south, suggesting that elevation above sea level is a driving factor shaping forest canopy height. The assessments, models, and maps provided by this study can be used by forest planners and land managers, as there is no reliable data on site productivity in the studied region.

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.

Windthrow damage detection in Nordic forests by 3D reconstruction of very high-resolution stereo optical satellite imagery

ABSTRACT We tested whether windthrow damage to Nordic conifer forest stands could be reliably detected as canopy height decrease between a pre-storm LiDAR (Light Detection and Ranging) digital surface model (DSM) and a photogrammetric DSM derived from a post-storm WorldView-3 stereo pair. The post-storm ground reference data consisted of field and unmanned aerial vehicle (UAV) observations of windthrow combined with no-damage areas collected by visual interpretation of the available very high resolution (VHR) satellite imagery. We trained and tested a thresholding model using canopy height change as the sole predictor. We undertook a two-step accuracy assessment by (1) running k-fold cross-validation on the ground reference dataset and examining the effect of the potential imperfections in the ground reference data, and (2) conducting rigorous accuracy assessment of the classified map of the study area using an extended set of VHR imagery. The thresholding model produced accurate windthrow maps in dense, productive forest stands with a sensitivity of 96%, specificity of 71%, and Matthews correlation coefficient (MCC) over 0.7. However, in sparse and high elevation stands, the classification accuracy was poor. Despite certain collection challenges during the winter months in the Nordic region, we consider VHR stereo satellite imagery to be a viable source of forest canopy height information and sufficiently accurate to map windthrow disturbance in forest stands of high to moderate density.

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.

A Rapid and Easy Way for National Forest Heights Retrieval in China Using ICESat-2/ATL08 in 2019

Continuous and extensive monitoring of forest height is essential for estimating forest above-ground biomass and predicting the ability of forests to absorb CO2. In particular, forest height at the national scale is an important indicator reflecting the national forestry economic construction, environmental governance, and ecological balance. However, the lack of inventory data restricts large-scale monitoring of forest height to some extent. Conducting manual surveys of forest height for large-scale areas would be labor-intensive and time-consuming. The successful launch of the new generation of spaceborne light detection and ranging (LiDAR) (The Ice, Cloud, and Land Elevation Satellite-2/the Advanced Topographic Laser Altimeter System, ICESat-2/ATLAS) has brought new opportunities for national-scale forestry resource surveys. This paper explores a method to survey national forest canopy height from the new generation of ICESat-2/ATLAS data. In view of the sparse sampling and little overlap between repeated spaceborne LiDAR data, a strategy for assessing the overall change of canopy height for large scales is provided. Some spatially continuous ancillary data were used to assist ICESat-2/ATLAS data to generate a wall-to-wall (spatially continuous) forest canopy height map in China by using the machine learning approach and then quantifying the analysis of forest canopy height in various provinces. The results show that there is a good correlation between the model forest height and the verification data, with a root mean squared error (RMSE) of 3.30 m and a coefficient of determination (R2) of 0.87. This indicates that the method for retrieving national forest canopy height is reliable. There are some limitations in areas with lower vegetation coverage or complex topography which need additional filtering or terrain correction to achieve higher accuracy in measuring forest canopy height. Our analysis suggests that ICESat-2/ATLAS data can achieve the retrieval of national forest height at an overall level, and it would be feasible to use ICESAT-2/ATLAS products to estimate forest canopy height change for large-scale areas.

Forest Age Mapping Using Landsat Time-Series Stacks Data Based on Forest Disturbance and Empirical Relationships between Age and Height

Forest age is a critical parameter for the status and potential of carbon sequestration in forest ecosystems and reflects major forest disturbance information. However, reliable forest age data with high spatial resolution are lacking to date. In this study, we proposed a forest age mapping method with a 30 m resolution that considers forest disturbance. Here, we used the Landsat time-series stacks (LTSS) data from 1986 to 2021 and implemented the LandTrendr algorithm on the Google Earth Engine (GEE) platform to detect the age of disturbed forests. The age of non-disturbed forests was extracted based on forest canopy height data and the empirical relationship between age and height. High-resolution Google images combined with the forest management archive data of forestry departments and national forest inventory (NFI) data were used for the validation of disturbed and non-disturbed forest age, respectively. The results showed that the LandTrendr algorithm detected disturbance years with producer and user accuracies of approximately 94% and 95%, respectively; and the age of non-disturbed forests obtained using the empirical age–height relationship showed an R2 of 0.8875 and a root mean squared error (RMSE) value of 5.776 with NFI-based results. This confirms the reliability of the proposed 30 m resolution forest age mapping method considering forest disturbance. Overall, the method can be used to produce spatially explicit forest age data with high resolution, which can contribute to the sustainable use of forest resources and enhance the understanding of carbon budget studies in forest ecosystems.

Elevated mortality rates of large trees allow for increased frequency of intermediate trees: A hypothesis supported by demographic model comparison with plot and LiDAR data

Tree diameter distributions are important indicators of forest structure, are a principal element of forest carbon stock estimates, and are theoutcome of forest demography. Hump-shaped tree diameter distributions, also known as rotated-sigmoid diameter distributions, are characterized by an increased frequency of intermediately-sized trees over what would be extrapolated from the small tree size distribution and are common in temperate forests. One hypothesis to explain hump-shaped tree size distributions is the U-shaped mortality curve, where the mortality rates of the largest canopy trees are higher than those of intermediately-sized canopy trees. However, studies that have directly tested this hypothesis by comparing tree diameter distributions predicted from forest demographic models with empirical diameter distributions are lacking, and how the U-shaped mortality curve influences other aspects of forest structure such as canopy height is not well understood. We used model-data comparisons to test the hypothesis that the U-shaped mortality curve generates the hump-shaped tree diameter distribution. We used two versions of a forest demographic model (the PPA model) to predict tree diameter distributions, one that assumed that tree demographic rates varied only between understory and canopy trees, and a second that assumed tree demographic rates varied among understory trees, small canopy trees, and large canopy trees, consistent with the U-shaped mortality curve. We compared predictions from each form of the model with empirical tree diameter distributions from the Wind River Forest Dynamics plot, a 27.2 ha forest dynamics plot with a hump-shaped diameter distribution. We found that the model that accounted for the higher mortality rates of larger canopy trees generated a hump-shaped distribution, whereas the model that did not account for the higher mortality rates of larger trees was not able to generate a hump-shaped diameter distribution. To understand how the assumptions of forest demographic patterns affected other aspects of forest structure that could be derived from remote sensing, we compared predictions of forest canopy height from each form of the model with measurements of forest canopy height derived from airborne LiDAR. We found that accounting for the higher mortality rates of larger trees became even more critical to accurate prediction of forest canopy height because intermediate-sized trees occupy large areas in the canopy. Our results demonstrate the link between the U-shaped mortality curve and hump-shaped diameter distributions, and suggest that accurate characterization of the demography of large trees may be particularly important for predicting forestcanopy structure and carbon stocks.

Comparative Study on Remote Sensing Methods for Forest Height Mapping in Complex Mountainous Environments

Forest canopy height is one of the critical parameters for carbon sink estimation. Although spaceborne lidar data can obtain relatively high precision canopy height on discrete light spots, to obtain continuous canopy height, the integration of optical remote sensing image data is required to achieve “from discrete to continuous” extrapolation based on different prediction models (parametric model and non-parametric model). This study focuses on the Shangri-La area and seeks to assess the practical applicability of two predictive models under complex mountainous conditions, using a combination of active and passive remote sensing data from ICESat-2 and Sentinel-2. The research aims to enhance our understanding of the effectiveness of these models in addressing the unique challenges presented by mountainous terrain, including rugged topography, variable vegetation cover, and extreme weather conditions. Through this work, we hope to contribute to the development of improved geospatial prediction algorithms for mountainous regions worldwide. The results show the following: (1) the fitting effect of the selected parametric model (empirical function regression) is poor in the area of Quercus acutissima and Pinus yunnanensis; (2) evaluation of the importance of each explanatory variable in the non-parametric model (random forest regression) shows that topographic and meteorological factors play a dominant role in canopy height inversion; (3) when random forest regression is applied to the inversion of canopy height, there is often a problem of error accumulation, which is of particular concern to the Quercus acutissima and Pinus yunnanensis; (4) the random forest regression with the optimal features has relatively higher precision by comparing the inversion accuracy of canopy height data of the empirical function regression, random forest regression with all features, and random forest regression with the optimal features in the study area, i.e., R2 (coefficient of determination) = 0.865 and RMSE (root mean square error) = 3.184 m. In contrast, the poor estimation results reflected by the empirical function regression, mainly resulting from the lack of consideration of topographic and meteorological factors, are not applicable to the inversion of canopy height under complex topographic conditions.

Object-based mapping and classification features for tropical highlands using on Sentinel-1, Sentinel-2, and GEDI canopy height data - A case study of the Muringato catchment, Kenya

Tropical highlands remain a challenging target for remote sensing due to their high heterogeneity of the landscape and frequent cloud cover, causing a shortage of high-quality and reliable comprehensive data on land use and land cover on a local or regional scale. These, however, are urgently needed by local stakeholders and decisionmakers. This applies for example to the Muringato sub-catchment in Nyeri County, Kenya, where acute water problems have been identified to be usually directly related to specific land use and land cover. This article contributes to the understanding of tropical highlands from a remote sensing perspective by examining Sentinel-1, Sentinel-2 and Global Forest Canopy Height Model data from the Global Ecosystem Dynamics Investigation, all provided by the Google Earth Engine. To do so, we assess classifiers derived from these datasets for different land cover types, analyzing the performance of promising candidates identified in the literature, using 2,800 samples extracted from high-resolution image data across Nyeri County. We also propose an object-based classification strategy based on sequential masking. This strategy is adapted to very heterogeneous landscapes by refining image objects after re-evaluating their homogeneity. Small buildings, which constitute a significant part of the settlement structure in the area, are particularly difficult to detect. To improve the recognition of these objects we additionally consider the local contrast of the relevant classifier to identify potential candidates. Evaluating our sample data, we found that especially optical indices like the Sentinel Water Index, the Enhanced Normalized Difference Impervious Surfaces Index or specific Sentinel-2 bands combined with canopy height data are promising for water, built-up or tree cover detection. With these findings, our proposed object-based classification approach is applied to the Muringato sub-catchment as a representative example of the Kenyan tropical highland region. We achieve a classification accuracy of approximately 88% in the Muringato sub-catchment, outperforming existing products available for the study area. The knowledge gained in the study will also be used for future remote sensing-based monitoring of the region.

Forest Structure Characterization in Germany: Novel Products and Analysis Based on GEDI, Sentinel-1 and Sentinel-2 Data

Monitoring forest conditions is an essential task in the context of global climate change to preserve biodiversity, protect carbon sinks and foster future forest resilience. Severe impacts of heatwaves and droughts triggering cascading effects such as insect infestation are challenging the semi-natural forests in Germany. As a consequence of repeated drought years since 2018, large-scale canopy cover loss has occurred calling for an improved disturbance monitoring and assessment of forest structure conditions. The present study demonstrates the potential of complementary remote sensing sensors to generate wall-to-wall products of forest structure for Germany. The combination of high spatial and temporal resolution imagery from Sentinel-1 (Synthetic Aperture Radar, SAR) and Sentinel-2 (multispectral) with novel samples on forest structure from the Global Ecosystem Dynamics Investigation (GEDI, LiDAR, Light detection and ranging) enables the analysis of forest structure dynamics. Modeling the three-dimensional structure of forests from GEDI samples in machine learning models reveals the recent changes in German forests due to disturbances (e.g., canopy cover degradation, salvage logging). This first consistent data set on forest structure for Germany from 2017 to 2022 provides information of forest canopy height, forest canopy cover and forest biomass and allows estimating recent forest conditions at 10 m spatial resolution. The wall-to-wall maps of the forest structure support a better understanding of post-disturbance forest structure and forest resilience.

Correction of phenology-induced effects in forest canopy height models based on airborne laser scanning data. Insights from the deciduous mountain forests in Picos de Europa National Park in Spain

The comparison of ALS time-series is an important element of modern landscape conservation planning, especially to monitor forest ecosystems. Modellers must evaluate phenology when comparing ALS-based maps of ground elevation or canopy height between years. We showcase the scenario using a National Park in the North-West of Spain where bi-temporal ALS has been used to map deciduous mountain forests. We compare the 2010 and 2021 surveys using the same algorithms to interpret ALS data and to generate actionable products for managers, mainly digital terrain models and canopy height models. We implemented a hybrid approach to correct the discrepancies between the surveys, showing the problems arising from differences in phenology or the selection of the scaling. We quantified around 5% of the area suffered from >1 m difference in DTM and higher impact on CHM values. With the hybrid method, modellers can highly reduce the uncertainty when comparing two ALS surveys and derivable products. We provided a solid graphical and analytical diagnosis of these emerging problems in the context of multi-temporal ALS surveys testing the hybrid approach at two resolutions: 1 and 2 m, fine-grained scales. The assessment of phenology-induced effects is important under the context of nationwide ALS survey programmes currently in operation and on high-demand. Finally, we discuss and frame the hybrid-approach as a well-suited vector of canopy gap detection methods to support conservation planning and enforce species-specific habitat improvements.

Estimating and mapping forest age across Canada's forested ecosystems

Forest age is an important variable for assessments of biodiversity and habitat, sustainable forest and land management, as well as forest carbon science and modeling. Tree and stand age are typically measured directly on site, or estimated through visual photo interpretation, with spatially explicit maps of forest age not often produced over large areas. Remote sensing enables the generation of wall-to wall, spatially explicit maps of disturbance events within the satellite record; however, as disturbance is relatively rare on the landscape in a given year, additional means of determining forest age are required. As reviewed herein, the estimation of forest age using optical Earth observation data is challenging due to the limited spectral link to the attribute of interest, especially as forests get older. The temporally dictated multi-method approach to forest age estimation outlined herein acknowledges these limitations, by applying the approach that is best suited to the quality of the information available, depending on the epoch of interest. In this research, we combine three approaches to estimate forest age at a 30-m spatial resolution using Landsat data. The first approach uses change detection protocols to detect disturbance from 1985 to 2019, with time since disturbance used as a proxy for forest age. The second approach uses Landsat surface reflectance composites to identify pixels exhibiting evidence of recovery from a disturbance that occurred within the twenty years prior to 1985, allowing for the extension of forest age estimates to 1965. Finally, given an understanding of the linkage between forest age and canopy height, inverted allometric equations are coupled with maps of forest structure and productivity metrics to model forest age for those pixels that show no evidence of disturbance or recovery to a maximum of 150 years, acknowledging that uncertainty in age estimate increases with increasing age. Combining these three approaches, forest age estimates are made for every treed pixel found within the 650 Mha forested ecosystems of Canada. Nationwide, mean estimated forest age for forests ≤150 years old (representing 94.1% of treed area) was 70 years (standard deviation = 32.1 years). For confidence building, forest age estimates were compared to reported forest age in the National Forest Inventory (NFI) both spatially and aspatially. Nationally, 5.9% of the forested area was estimated to be older than 150 years, while 9.5% of area within in the NFI sample was recorded as older than 150 years. The median estimated forest age for forested pixels ≤150 years old was 68 years while median forest age reported in the NFI was 73 years, with regional variability matching expectations related to disturbance regimes and productivity. Spatially explicit maps of forest age provide important information for understanding forest ecosystems and can be used to inform a wide range of policy, science, and management needs.

Comparison of GEDI LiDAR Data Capability for Forest Canopy Height Estimation over Broadleaf and Needleleaf Forests

The GEDI LiDAR system was specifically designed to detect vegetation structure and has proven to be a suitable tool for estimating forest biophysical parameters, especially canopy height, at a global scale. This study compares the GEDI relative height metric (RH100) over different forest types, especially deciduous broadleaf and evergreen coniferous located in Thuringia, Germany, to understand how the forest structural differences affect the GEDI height estimation. A canopy height model that was produced using digital terrain and surface models (DTM and DSM) derived from airborne laser scanning data is used as the reference data. Based on the result, GEDI canopy height over needleleaf forest is slightly more accurate (RMSE = 6.61 m) than that over broadleaf (RMSE = 8.30 m) and mixed (RMSE = 7.94 m) forest. Evaluation of the GEDI acquisition parameters shows that differences in beam type, sensitivity, and acquisition time do not significantly affect the accuracy of canopy heights, especially over needleleaf forests. Considering foliage condition impacts on canopy height estimation, the contrasting result is observed in the broadleaf and needleleaf forests. The GEDI dataset acquired during the winter when deciduous species shed their leaves (the so-called leaf-off dataset), outperforms the leaf-on dataset in the broadleaf forest but shows less accurate results for the needleleaf forest. Considering the effect of the plant area index (PAI) on the accuracy of the GEDI canopy height, the GEDI dataset is divided into two sets with low and high PAI values with a threshold of median PAI = 2. The results show that the low PAI dataset (median PAI < 2) corresponds to the non-growing season (autumn and winter) in the broadleaf forest. The slightly better performance of GEDI using the non-growing dataset (RMSE = 7.40 m) compared to the growing dataset (RMSE = 8.44 m) in the deciduous broadleaf forest and vice versa, the slightly better result using the growing dataset (RMSE = 6.38 m) compared to the non-growing dataset (RMSE = 7.24 m) in the evergreen needleleaf forest is in line with the results using the leaf-off/leaf-on season dataset. Although a slight improvement in canopy height estimation was observed using either the leaf-off or non-growing season dataset for broadleaf forest, and either the leaf-on or growing season dataset for needleleaf forest, the approach of filtering GEDI data based on such seasonal acquisition time is recommended when retrieving canopy height over pure stands of broadleaf or needleleaf species, and the sufficient dataset is available.

Performance evaluation of convolution neural networks in canopy height estimation using sentinel 2 data, application to Thailand

ABSTRACT Electric shorting induced by tall vegetation is one of the major hazards affecting power transmission lines extending through rural regions and rough terrain for tens of kilometres. This raises the need for an accurate, reliable, and cost-effective approach for continuous monitoring of canopy heights. This paper proposes and evaluates two deep convolution neural network (CNN) variants based on Seg-Net and Res-Net architectures, characterized by their small number of trainable weights (nearly 800,000) while maintaining high estimation accuracy. The proposed models utilize the freely available data from Sentinel-2, and a digital surface model to estimate forest canopy heights with high accuracy and a spatial resolution of 10 metres. Various factors affect canopy height estimation, including topography signature, dataset diversity, input layers, and model structure. The proposed models are applied separately to two powerline regions located in the northern and southern parts of Thailand. The application results show that the proposed Encoder-Decoder CNN Seg-Net model presents an average mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination of 1.38 m, 1.85 m, and 0.87, respectively, and is nearly 4.8 times faster than the CNN Res-Net model in conversion. These results prove the proposed model’s capability of estimating and monitoring canopy heights with high accuracy and fine spatial resolution.

Mapping the Forest Height by Fusion of ICESat-2 and Multi-Source Remote Sensing Imagery and Topographic Information: A Case Study in Jiangxi Province, China

Forest canopy height is defined as the distance between the highest point of the tree canopy and the ground, which is considered to be a key factor in calculating above-ground biomass, leaf area index, and carbon stock. Large-scale forest canopy height monitoring can provide scientific information on deforestation and forest degradation to policymakers. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was launched in 2018, with the Advanced Topographic Laser Altimeter System (ATLAS) instrument taking on the task of mapping and transmitting data as a photon-counting LiDAR, which offers an opportunity to obtain global forest canopy height. To generate a high-resolution forest canopy height map of Jiangxi Province, we integrated ICESat-2 and multi-source remote sensing imagery, including Sentinel-1, Sentinel-2, the Shuttle Radar Topography Mission, and forest age data of Jiangxi Province. Meanwhile, we develop four canopy height extrapolation models by random forest (RF), Support Vector Machine (SVM), K-nearest neighbor (KNN), Gradient Boosting Decision Tree (GBDT) to link canopy height in ICESat-2, and spatial feature information in multi-source remote sensing imagery. The results show that: (1) Forest canopy height is moderately correlated with forest age, making it a potential predictor for forest canopy height mapping. (2) Compared with GBDT, SVM, and KNN, RF showed the best predictive performance with a coefficient of determination (R2) of 0.61 and a root mean square error (RMSE) of 5.29 m. (3) Elevation, slope, and the red-edge band (band 5) derived from Sentinel-2 were significantly dependent variables in the canopy height extrapolation model. Apart from that, Forest age was one of the variables that the RF moderately relied on. In contrast, backscatter coefficients and texture features derived from Sentinel-1 were not sensitive to canopy height. (4) There is a significant correlation between forest canopy height predicted by RF and forest canopy height measured by field measurements (R2 = 0.69, RMSE = 4.02 m). In a nutshell, the results indicate that the method utilized in this work can reliably map the spatial distribution of forest canopy height at high resolution.

A Method for Forest Canopy Height Inversion Based on UAVSAR and Fourier–Legendre Polynomial—Performance in Different Forest Types

Mapping forest canopy height at large regional scales is of great importance for the global carbon cycle. Polarized interferometric synthetic aperture radar is an efficient and irreplaceable remote sensing tool. Developing an efficient and accurate method for forest canopy height estimation is an important issue that needs to be addressed urgently. In this paper, we propose a novel four-stage forest height inversion method based on a Fourier–Legendre polynomial (FLP) with reference to the RVoG three-stage method, using the multi-baseline UAVSAR data from the AfriSAR project as the data source. The third-order FLP is used as the vertical structure function, and a small amount of ground phase and LiDAR canopy height is used as the input to solve and fix the FLP coefficients to replace the exponential function in the RVoG three-stage method. The performance of this method was tested in different forest types (mangrove and inland tropical forests). The results show that: (1) in mangroves with homogeneous forest structure, the accuracy based on the four-stage FLP method is better than that of the RVoG three-stage method. For the four-stage FLP method, R2 is 0.82, RMSE is 6.42 m and BIAS is 0.92 m, while the R2 of the RVoG three-stage method is 0.77, RMSE is 7.33 m, and bias is −3.49 m. In inland tropical forests with complex forest structure, the inversion accuracy based on the four-stage FLP method is lower than that of the RVoG three-stage method. The R2 is 0.50, RMSE is 11.54 m, and BIAS is 6.53 m for the four-stage FLP method; the R2 of the RVoG three-stage method is 0.72, RMSE is 8.68 m, and BIAS is 1.67 m. (2) Compared to the RVoG three-stage method, the efficiency of the four-stage FLP method is improved by about tenfold, with the reduction of model parameters. The inversion time of the FLP method in a mangrove forest is 3 min, and that of the RVoG three-stage method is 33 min. In an inland tropical forest, the inversion time of the FLP method is 2.25 min, and that of the RVoG three-stage method is 21 min. With the application of large regional scale data in the future, the method proposed in this study is more efficient when conditions allow.

Measuring forest height from space. Opportunities and limitations observed in natural forests

The goal of the study is to test a methodology that outputs continuous sets of data by integrating forest canopy height satellite measurements in a Random Forest (RF) algorithm. We assess the performance of the two spaceborne LiDAR missions by comparing them to field measurements taken with a mobile LiDAR scanner (MLS), then we test the compatibility of the sensors as an input for an RF model. The Spearman analysis showed a significant correlation between the MLS height and the GEDI height (p = 0.00015), but not for the ICEsat-2 height measurements (p = 0.3). The same analysis proved to be inconclusive for the RF output, where, although the p-value was 0.00025, the R value was only 0.38. Although the method studied in this paper proved to be only fit for estimation purposes, the integration of spaceborne LiDAR sensors in the RF shows high potential for obtaining homogenous, continuous sets of data.

Tropical Forest Top Height by GEDI: From Sparse Coverage to Continuous Data

Estimating consistent large-scale tropical forest height using remote sensing is essential for understanding forest-related carbon cycles. The Global Ecosystem Dynamics Investigation (GEDI) light detection and ranging (LiDAR) instrument employed on the International Space Station has collected unique vegetation structure data since April 2019. Our study shows the potential value of using remote-sensing (RS) data (i.e., optical Sentinel-2, radar Sentinel-1, and radar PALSAR-2) to extrapolate GEDI footprint-level forest canopy height model (CHM) measurements. We show that selected RS features can estimate vegetation heights with high precision by analyzing RS data, spaceborne GEDI LiDAR, and airborne LiDAR at four tropical forest sites in South America and Africa. We found that the GEDI relative height (RH) metric is the best at 98% (RH98), filtered by full-power shots with a sensitivity greater than 98%. We found that the optical Sentinel-2 indices are dominant with respect to radar from 77 possible features. We proposed the nine essential optical Sentinel-2 and the radar cross-polarization HV PALSAR-2 features in CHM estimation. Using only ten optimal indices for the regression problems can avoid unimportant features and reduce the computational effort. The predicted CHM was compared to the available airborne LiDAR data, resulting in an error of around 5 m. Finally, we tested cross-validation error values between South America and Africa, including around 40% from validation data in training to obtain a similar performance. We recommend that GEDI data be extracted from all continents to maintain consistent performance on a global scale. Combining GEDI and RS data is a promising method to advance our capability in mapping CHM values.

Analysis of polarimetric GNSS-R Stokes parameters of the Earth's land surface

Polarimetric Global Navigation Satellite System – Reflectometry (GNSS-R) has proven to be sensitive to different land parameters, such as vegetation or soil moisture. In this manuscript, the correlation of the Stokes parameters of the GNSS reflections collected by the Soil Moisture Active Passive - Reflectometry (SMAP-R) with SMAP soil moisture (SM), SMAP vegetation optical depth (VOD), and the Global Forest Canopy Height (CH) are analyzed. SMAP-R is the first hybrid compact polarimetric reflectometer in space, and it provides a valuable dataset useful to design future GNSS-R mission that target polarimetric retrievals. The SMAP-R total-intensity reflectivity is presented, estimated from the bistatic radar cross section assuming coherent reflection. The calibrated intensity and the normalized Stokes parameters of the received wave are compared for a 1-year period to the SMAP VOD, the SMAP SM, the Global Forest CH, and the standard deviation of the digital elevation model. To provide a first assessment of the performance of full-Polarimetric GNSS-R to estimate SM and VOD, a linear regression approach is implemented using the four Stokes parameters and the child Stokes parameters. The two approaches show a correlation coefficient of R = 0.73 and R = 0.55 to estimate SM and VOD, respectively, enabling the estimation of both variables using polarimetric GNSS-R data without the need for ancillary data. Finally, the differences between the polarimetric signature at different incidence angles and polarizations are discussed.

Positive effects of projected climate change on post-disturbance forest regrowth rates in northeastern North American boreal forests

Forest anthropogenic and natural stand-replacing disturbances are increasing worldwide due to global change. Many uncertainties regarding the regeneration and growth of these young forests remain within the context of changing climate. In this study, we investigate the effects of climate, tree species composition, and other landscape-scale environmental variables upon boreal forest regrowth following clearcut logging in eastern Canada. Our main objective was to predict the effects of future climate changes upon post-logging forest height regrowth at a subcontinental scale using high spatial resolution remote sensing data. We modeled forest canopy height (estimated from airborne laser scanning [LiDAR] data over 20 m resolution virtual plots) as a function of time elapsed since the last clearcut along with climate (i.e. temperature and moisture), tree species composition, and other environmental variables (e.g. topography and soil hydrology). Once trained and validated with ∼240 000 plots, the model that was developed in this study was used to predict potential post-logging canopy height regrowth at 20 m resolution across a 240 000 km2 area following scenarios depicting a range of projected changes in temperature and moisture across the region for 2041–2070. Our results predict an overall beneficial, but limited effect of projected climate changes upon forest regrowth rates in our study area. Stimulatory effects of projected climate change were more pronounced for conifer forests, with growth rates increasing between +5% and +50% over the study area, while mixed and broadleaved forests recorded changes that mostly ranged from −5% to +35%. Predicted increased regrowth rates were mainly associated with increased temperature, while changes in climate moisture had a minor effect. We conclude that such growth gains could partially compensate for the inevitable increase in natural disturbances but should not allow any increase in harvested volumes.