Forest Height Research Articles

Widespread degradation and limited protection of forests in global tropical dry ecosystems

Despite their ecological and socioeconomic importance, the forested areas of tropical dry ecosystems remain among the most threatened biomes worldwide. There has been limited research on the current extent, fragmentation, and conservation status of tropical dry forests on a global scale. In this study, we used the GLAD UMD Forest Height dataset to provide a high-resolution global assessment of the extent, patch size distribution, and protection levels of Tropical and Subtropical Dry Broadleaf Forests (TBDF) and forested areas of the Tropical and Subtropical Grasslands, Savannas, and Shrublands (TSGSS) by ecoregion and country. We find that despite increasing protection, degradation continues to rise with less than ⅓ of the original TBDF and TSGSS currently forested and over 80 % of those forests currently exposed to edge effects. <1 % of the remaining forest patches are large (>10 km2), and <25 % of the remaining forests are located in conservation or protection zones (TBDF:16 %; TSGSS:21 %), with a heavy protection bias in S.E. Asia. These results indicate high levels of degradation and low conservation leading to potentially severe ecological consequences. Systematic and coordinated efforts are needed to reduce the pressure on this ecosystem and ensure the ongoing provisioning of biodiversity and ecosystem services in these ecoregions.

Spatial and temporal variation of three Eddy-Covariance flux footprints in a Tropical Dry Forest

Tropical Dry Forest (TDF) is a distinctive ecosystem characterized by distinct seasonal changes, primarily composed of deciduous trees adapted to endure prolonged drought periods. Obtaining ecosystem-scale flux measurements using the Eddy Covariance (EC) technique is crucial for comprehending the spatiotemporal dynamics of interactions between the land and the atmosphere. It is imperative to have information about the variability in the contribution source area of the Flux Footprint Area (FFA) to assess the impact of changing microclimate variables on the EC FFA. In this study, we describe and evaluate the influence of various phases of the El Niño Southern Oscillation (ENSO) on the spatial and temporal climatology of FFA at three EC sites within the TDF of the Santa Rosa National Park-Environmental Super Site (SRNP-EMSS). Our analysis covered the period from 2014 to 2021 on a seasonal scale and employed a 2D model. Our findings indicate that: a) seasonal climatology FFAs exhibit variation across locations and over time due to factors such as forest canopy height, site topography, wind direction, and atmospheric stability conditions; b) FFAs are smaller during dry seasons compared to wet seasons; and c) multiple ENSO phases (cold, neutral, and warm) significantly affect the seasonal climatology of FFAs. Notably, during the moderate La Niña phase (2020/2021), the mean FFA size was 4.20 km2, which was 23.80% larger than the mean FFA during the intense El Niño phase (2015/2016) at 3.20 km2. Additionally, during La Niña phases, FFAs were more asymmetrically distributed around the EC tower compared to El Niño phases, with mean symmetric indices of 0.35 and 0.29, respectively. Finally, the overlap among the three EC climatology FFAs during La Niña phases was 35% greater than that during the El Niño phases. Our study highlights how variations in FFAs can significantly impact intra-annual flux estimates.

A HIGH-PRECISION EXPLICIT FOREST CARBON STOCK MODEL BASED ON REMOTE SENSING

Abstract. The current status as well as the potential absorption capability of the forest carbon sinks in terrestrial ecosystems are in urgent need of further studies. The ground observation-based methods are labor-intensive, and the resulting statistics from samples are difficult to evaluate, while inversion methods based on remote sensing data lack theoretical explanation and universality. This paper proposes a pixel-level, multi-scale, high-precision Explicit Forest carbon stock Model (EFM) that is universal and adaptive. First, four key variables were used in the construction of the EFM: remote sensing image resolution, forest canopy density, terrain slope, and forest height; Second, simulated forest scene were generated based on the growth characteristics of individual trees, and EFM parameters were solved and analyzed by these simulated pixels; Third, the EFM was tested at various scales and forest saturation levels to verify its accuracy, robustness, and applicability, the simulation and real-life experiments show that the correlation coefficient is greater than 0.98 and the relative error is about 20%. The EFM solves the problem that the existing methods are lack in theoretical interpretation and universal applicability, thus can be used to map forest carbon stocks at high resolution and large scale and even monitor forest carbon dynamics at global scale.

State-wide forest canopy height and aboveground biomass map for New York with 10 m resolution, integrating GEDI, Sentinel-1, and Sentinel-2 data

Investigating the quantity of forest aboveground biomass (AGB) is crucial for understanding the role forests play in the global carbon cycle. The canopy height model (CHM) is a critical component in estimating AGB, as it provides a three-dimensional representation of the tree canopy. Traditional CHM estimation methods are time-consuming, labor-intensive, and expensive, particularly at large-scales. Remote sensing is a cost-effective and efficient alternative approach, providing valuable information over large areas in a timely manner. The Global Ecosystem Dynamics Investigation (GEDI) onboard the International Space Station is a space-based light detection and ranging (LiDAR) system designed to collect information on vertical structures of vegetation. One major problem with the collection of GEDI data is that it provides limited information over discrete ground samples, also known as footprints, and thus do not provide wall-to-wall gridded height products. The objective of this study was twofold: a) to integrate the GEDI LiDAR footprint heights with Sentinel-2 multispectral imagery to generate a 10 m wall-to-wall CHM map of New York State (NYS), USA for the year 2019 and b) to improve our previously generated AGB map (both accuracy and resolution) of NYS for the year 2019 by fusing Sentinel-2 multispectral, Sentinel-1 synthetic aperture radar (SAR), and the produced CHM. To generate the 10 m CHM map, the GEDI footprints height measurements were extrapolated using Sentinel-2 imagery and a random forest model. The CHM that was produced was assessed by using GEDI footprints that were not part of the training phase and were therefore independent (extrapolated). Comparing our 10 m CHM with the available global 30 m CHM map provided by Potapov et al. (2021) over NYS shows significant improvement not only in terms of spatial resolution, but also in terms of accuracy. The root mean square error (RMSE) of our 10 m CHM is 4.4 m while this value is 7.49 m for the 30 m CHM over NYS. Similarly, the R2 value for the 10 m CHM map is 0.74, while that of the 30 m CHM is 0.46. Finally, the integration of produced 10 m CHM, Sentinel-1, and Sentinel-2 datasets were utilized to create a 10 m AGB map of NYS with the RMSE of 39.49 Mg/ha, and R2 of 0.65. The results demonstrate the potential of integrating GEDI, Sentinel-1, and Sentinel-2 data for providing a valuable tool for large-scale mapping of forest canopy structure and biomass, which can help to inform forest management and carbon accounting efforts.

Forest age estimation in northern Arkhangelsk region based on machine learning pipeline on Sentinel-2 and auxiliary data

Tree age is one of the key characteristics of a forest, along with tree species and height. It affects management decisions of forest owners and allows researchers to analyze environmental characteristics in support of sustainable development. Although forest age is of primary significance, it can be unknown for remote areas and large territories. Currently, remote sensing (RS) data supports rapid information gathering for wide territories. To automate RS data processing and estimate forest characteristics, machine learning (ML) approaches are applied. Although there are different data sources that can be used as features in ML models, there is no unified strategy on how to prepare a dataset and define a training task to estimate forest age. Therefore, in this work, we aim to conduct a comprehensive study on forest age estimation using remote sensing observations of the Sentinel-2 satellite and two ML-based approaches for forestry inventory data, namely stand-based and pixel-based. We chose the CatBoost algorithm to assess these two approaches. To establish the robustness of the pipeline, an in-depth analysis is conducted, embracing diverse scenarios incorporating dominant species information, tree height, Digital Elevation Model (DEM), and vegetation indices. We performed experiments on forests in the northern Arkhangelsk region and obtained the best Mean Absolute Error (MAE) result of 7 years in the case of the stand-based approach and 6 years in the case of the pixel-based approach. These results are achieved for all available input data such as spectral satellites bands, vegetation indices, and auxiliary forest characteristics (dominant species and height). However, when only spectral bands are used, the MAE metric is the same both for per-pixel and per-stand approaches and equals 11 years. It was also shown that, despite high correlation between forest age and height, only height can not be used for accurate age estimation: the MAE increases to 18 and 26 years for per-pixel and per-stand approaches, respectively. The conducted study might be useful for further investigation of forest ecosystems through remote sensing observations.

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.

Относительная высота деревьев в изолированных популяциях сосновых древостоев

The study of relative tree height in lingonberry, bilberry and sphagnum pine forests on Bolshoy Solovetsky Island was carried out. The island is the largest by area in the Solovetsky Archipelago and in the White Sea. The archipelago is a UNESCO World Heritage Site. Relative height is an important indicator characterizing the growth of woody plants and reflecting how many centimeters of height growth for each centimeter of diameter. We laid 34 sample plots in the most widespread forest types – lingonberry, bilberry, and sphagnum pine forests, occupying 82.0 % of the total pine forest area. At each sampling area we selected survey trees (64 trees each), measured heights, diameters, and took cores with an age drill at the root neck to determine the age. A total of 2176 trees were used in the relative height analysis. The average relative height in lingonberry, bilberry and sphagnum pine forests on Bolshoy Solovetsky Island is 62.5; 61.5 and 54.7 cm/cm, respectively. This relative height is noticeably lower than in pine forests on the mainland in the Arkhangelsk Oblast. There, the values are 84.9; 84.9 and 79.2 cm/cm for the respective forest types. It is not possible to use the existing growth progress tables developed on the basis of mainland trees for island pine forests. Equations for determining the relative height of pine forests on Bolshoy Solovetsky Island were obtained. It is proposed to use tree diameter rather than age as an input parameter, since the determination of age is much more difficult than that of diameter at breast height. For citation: Sobolev A.N., Feklistov P.A., Neverov N.A., Makarov S.S. Relative Tree Height in Isolated Populations of Pine Stands. Lesnoy Zhurnal = Russian Forestry Journal, 2023, no. 6, pp. 102–113. (In Russ.). https://doi.org/10.37482/0536-1036-2023-6-102-113

High-Resolution Mapping of Mangrove Species Height in Fujian Zhangjiangkou National Mangrove Nature Reserve Combined GF-2, GF-3, and UAV-LiDAR

Mangroves as an important blue carbon ecosystem have a unique ability to sequester and store large amounts of carbon. The height of mangrove forest is considered to be a critical factor in evaluating carbon sink capacity. However, considering the highly complicated nature of the mangrove system, accurate estimation of mangrove species height is challenging. Gaofen-2 (GF-2) panchromatic and multispectral sensor (PMS), Gaofen-3 (GF-3) SAR images, and unmanned aerial vehicle-light detection and ranging (UAV-LiDAR) data have the capability to capture detailed information about both the horizontal and vertical structures of mangrove forests, which offer a cost-effective and reliable approach to predict mangrove species height. To accurately estimate mangrove species height, this study obtained a variety of characteristic parameters from GF-2 PMS and GF-3 SAR data and utilized the canopy height model (CHM) derived from UAV-LiDAR data as the observed data of mangrove forest height. Based on these parameters and the random forest (RF) regression algorithm, the mangrove species height result had a root-mean-square error (RMSE) of 0.91 m and an R2 of 0.71. The Kandelia obovate (KO) exhibited the tallest tree height, reaching a maximum of 9.6 m. The polarization features, HH, VV, and texture feature, mean_1 (calculated based on the mean value of blue band in GF-2 image), had a reasonable correlation with canopy height. Among them, the most significant factor in determining the height of mangrove forest was HH. In areas where it is difficult to conduct field surveys, the results provided an opportunity to update access to acquire forest structural attributes.

Climate change altered the dynamics of stand dominant height in forests during the past century – Analysis of 20 European tree species

Analyzing how climate change has affected forest growth is crucial for predicting future dynamics and adapting forest management to future climate change. In this paper, we investigate how climate change has modified stand dominant height dynamics and site index of 20 European tree species. We used an innovative method based on an annual height increment equation to model stand dominant height as a function of climate back to 1872 and of other stand environmental conditions. We used these models to simulate stand dominant height dynamics and site index under two different climates (prior to climate change and actual recent climate) to analyze the impact of climate change over the past century. To build our models, we combined the recently published FYRE long-term climate database, which provides daily data since 1871, with data from more than 17,000 forest stands of the French National Forest Inventory network. Higher temperature, precipitation and climatic water balance generally favor stand dominant height dynamics when the variables are considered separately. However, the positive effects often saturate at the higher end of the variable distribution. Over the past century, the effect of climate change on the site index has varied widely among species, ranging from a decrease of less than 3% to an increase of more than 5%. The effect of climate change has also varied within species, with more positive effects on initially temperature-limited stands for some species. For the species and environmental conditions considered, our results highlight a positive response of site index to past climate change for most species, albeit with between- and within-species differences. Our results also suggest that this positive response could become negative under continued climate change. These conclusions, as well as the quantitative relationships we provide between climate and stand dominant height dynamics or site index, will help design management strategies to adapt forests to climate change.

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

&lt;span lang="EN-US"&gt;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.&lt;/span&gt;

Effect of leaf-on and leaf-off canopy conditions on forest height retrieval and modelling with ICESat-2 data

ABSTRACT Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) provides effective photon-counting light detection and ranging (LiDAR) data for estimating forest height across extensive geographical areas. Although prior studies have illustrated canopy conditions during leaf-on and leaf-off phases may influence ICESat-2 derived forest heights, a comprehensive understanding of this effect remains incomplete. This study seeks to comprehensively assess how varying canopy conditions (leaf-on/leaf-off) affect ICESat-2 forest height retrieval and modelling. First, the accuracies of ICESat-2 terrain and canopy heights under leaf-on and leaf-off conditions were validated. Second, random forest algorithm was utilized to model forest height by integrating ICESat-2, Sentinel-2, and other ancillary datasets. Finally, we evaluated the influence of leaf-on and leaf-off conditions on forest height retrieval and modelling. Results reveal higher consistency between ICESat-2 and airborne LiDAR-derived terrain heights compared to the agreement between two canopy height datasets. Accuracies of ICESat-2 terrain and canopy heights are higher under leaf-off conditions in contrast to leaf-on conditions. Notably, the accuracies of ICESat-2 terrain and canopy heights under various conditions are closely linked to canopy cover. Furthermore, the accuracy of forest height modelling can be enhanced by combining ICESat-2 data collected during both leaf-on and leaf-off seasons with further eliminating low-quality samples.

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

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

Improved Mapping of Regional Forest Heights by Combining Denoise and LightGBM Method

Currently, the integration of satellite-based LiDAR (ICESat-2) and continuous remote sensing imagery has been extensively applied to mapping forest canopy height over large areas. A considerable fraction of low-quality photons exists in ICESAT-2/ATL08 products, which restricts the performance of regional canopy height estimation. To solve these problems, a Local Noise Removal-Light Gradient Boosting Machine (LNR-LGB) method was proposed in this study, which efficiently filtered the unreliable canopy photons in ATL08, constructed an extrapolation model by combining multiple remote sensing data, and finally mapped the 30 m forest canopy height of Hunan Province in 2020. To verify the feasibility of this method, the canopy parameters were also filtered based on ATL08 product attributes (traditional method), and the accuracy of the two models was compared using the 10-fold cross-validation. The conclusions were as follows: (1) compared with the traditional model, the overall accuracy of the LNR-LGB model was approximately doubled, in which R2 increased from 0.46 to 0.65 and RMSE decreased from 6.11 m to 3.48 m; (2) the forest height in Hunan Province ranged from 2.53 to 50.79 m with an average value of 18.34 m. The LNR-LGB method will provide a new concept for achieving high-accuracy mapping of regional forest height.

Machine learning for modeling forest canopy height and cover from multi-sensor data in Northwestern Ethiopia.

Continuous mapping of the height and canopy cover of forests is vital for measuring forest biomass, monitoring forest degradation and restoration. In this regard, the contribution of Light Detection and Ranging (LiDAR) sensors, which were developed to obtain detailed data on forest composition across large geographical areas, is immense. Accordingly, this study aims to predict forest canopy cover and height in tropical forest areas utilizing Global Ecosystem Dynamics Investigation (GEDI) LIDAR, multisensor images, and random forest regression. To achieve this, we gathered predictor variables from the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), Sentinel-2 multispectral datasets, and Sentinel-1 synthetic aperture radar (SAR) backscatters. The model's accuracy was evaluated based on a validation dataset of GEDI Level 2A and Level 2B. The random forest method was used the combination of data layers from Sentinel-1, Sentinel-2, and topographic measurements to model forest canopy cover and height. The produced canopy height and cover maps had a resolution of 30m with R2 = 0.86 and an RMSE of 3.65m for forest canopy height and R2 = 0.87 and an RMSE of 0.15 for canopy cover for the year 2022. These results suggest that combining multiple variables and data sources improves canopy cover and height prediction accuracy compared to relying on a single data source. The output of this study could be helpful in creating forest management plans that support sustainable utilization of the forest resources.

Optimization of the Vertical Wavenumber for PolInSAR Inversion Performance Based on Numerical CRLB Analysis

A number of advanced SAR missions have been planned to launch, which can operate in fully polarimetric SAR interferometry mode to acquire structural parameters of global forests. Before the PolInSAR mission, the system configuration of vertical wavenumber kz must be carefully designed because it has a significant impact on the inversion performance. To minimize the estimation error of forest height caused by the system error from the future PolInSAR campaigns, it is valuable for us to optimize the vertical wavenumber. To quantitatively investigate the impact of kz on PolInSAR inversion performance, this paper proposes the optimization of kz based on the Cramér–Rao Lower Bound (CRLB) analysis. Extensive numerical CRLB simulations have been conducted to analyze the impact of several parameters, including extinction level, incident angle, and system decorrelation, etc., on the optimum kz. Finally, by minimizing the simulated CRLB, the numerical optimum kz maps are provided for the system engineers to easily design the system parameters.

How to adequately determine the top height of forest stands based on airborne laser scanning point clouds?

Top height (TH) is one of the most important features of forest stands that is used in forest resource management. Thus far, the TH has been mainly determined in forestry on the basis of field measurements. There are certain accepted theoretical guidelines on how the TH should be determined. However, due to high cost and time consumption, it is difficult to meet these assumptions with traditional field measurement methods. Currently, remote sensing is widely used in forest inventory, also for the determination of TH. However, it is still usually conducted by linking the remote sensing data—most often airborne laser scanning (ALS) point clouds—with ground-based measurements using the area-based approach. Different plot sizes and point cloud metrics are used to determine TH, making it difficult to compare the results. There is therefore a need for a universal methodology for determining TH directly from ALS data – one that is consistent with the methodology used for field measurement data. This paper presents the results of an experiment that was aimed at assessing the accuracy of TH determination on the basis of ALS data. A comparison of the six considered ALS-derived proxies of TH showed that the individual tree detection approach was the most accurate. The bias of this method, when calculated for 48 artificial forest stands, amounted to 0.15 m. The approach based on the lower percentiles of the distribution of point heights significantly underestimated the TH from −0.67 m to up to −1.9 m. On the basis of the obtained results, we claim that TH can be determined, based on ALS data without ground measurements, with an accuracy that is acceptable for forest practitioners and researchers.

Aerodynamic effects cause higher forest evapotranspiration and water yield reductions after wildfires in tall forests.

Wildfires are increasing in frequency, intensity, and extent globally due to climate change and they can alter forest composition, structure, and function. The destruction and subsequent regrowth of young vegetation can modify the ecosystem evapotranspiration and downstream water availability. However, the response of forest recovery on hydrology is not well known with even the sign of evapotranspiration and water yield changes following forest fires being uncertain across the globe. Here, we quantify the effects of forest regrowth after catastrophic wildfires on evapotranspiration and runoff in the world's tallest angiosperm forest (Eucalyptus regnans) in Australia. We combine eddy covariance measurements including pre- and post-fire periods, mechanistic ecohydrological modeling and then extend the analysis spatially to multiple fires in eucalypt-dominated forests in south-eastern Australia by utilizing remote sensing. We find a fast recovery of evapotranspiration which reaches and exceeds pre-fire values within 2 years after the bushfire, a result confirmed by eddy covariance data, remote sensing, and modeling. Such a fast evapotranspiration recovery is likely generalizable to tall eucalypt forests in south-eastern Australia as shown by remote sensing. Once climate variability is discounted, ecohydrological modeling shows evapotranspiration rates from the recovering forest which reach peak values of +20% evapotranspiration 3 years post-fire. As a result, modeled runoff decreases substantially. Contrary to previous research, we find that the increase in modeled evapotranspiration is largely caused by the aerodynamic effects of a much shorter forest height leading to higher surface temperature, higher humidity gradients and therefore increased transpiration. However, increases in evapotranspiration as well as decreases in runoff caused by the young forest are constrained by energy and water limitations. Our result of an increase in evapotranspiration due to aerodynamic warming in a shorter forest after wildfires could occur in many parts of the world experiencing forest disturbances.

FORMS: Forest Multiple Source height, wood volume, and biomass maps in France at 10 to 30 m resolution based on Sentinel-1, Sentinel-2, and Global Ecosystem Dynamics Investigation (GEDI) data with a deep learning approach

Abstract. The contribution of forests to carbon storage and biodiversity conservation highlights the need for accurate forest height and biomass mapping and monitoring. In France, forests are managed mainly by private owners and divided into small stands, requiring 10 to 50 m spatial resolution data to be correctly separated. Further, 35 % of the French forest territory is covered by mountains and Mediterranean forests which are managed very extensively. In this work, we used a deep-learning model based on multi-stream remote-sensing measurements (NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission and ESA's Copernicus Sentinel-1 and Sentinel-2 satellites) to create a 10 m resolution canopy height map of France for 2020 (FORMS-H). In a second step, with allometric equations fitted to the French National Forest Inventory (NFI) plot data, we created a 30 m resolution above-ground biomass density (AGBD) map (Mg ha−1) of France (FORMS-B). Extensive validation was conducted. First, independent datasets from airborne laser scanning (ALS) and NFI data from thousands of plots reveal a mean absolute error (MAE) of 2.94 m for FORMS-H, which outperforms existing canopy height models. Second, FORMS-B was validated using two independent forest inventory datasets from the Renecofor permanent forest plot network and from the GLORIE forest inventory with MAE of 59.6 and 19.6 Mg ha−1, respectively, providing greater performance than other AGBD products sampled over France. Finally, we compared FORMS-V (for volume) with wood volume estimations at the ecological region scale and obtained an R2 of 0.63 with an MAE of 30 m3 ha−1. These results highlight the importance of coupling remote-sensing technologies with recent advances in computer science to bring material insights to climate-efficient forest management policies. Additionally, our approach is based on open-access data having global coverage and a high spatial and temporal resolution, making the maps reproducible and easily scalable. FORMS products can be accessed from https://doi.org/10.5281/zenodo.7840108 (Schwartz et al., 2023).

20th-Century hurricanes leave long-lasting legacies on tropical forest height and the abundance of a dominant wind-resistant palm.

Projected increases in hurricane intensity under a warming climate will have profound effects on many forest ecosystems. One key challenge is to disentangle the effects of wind damage from the myriad factors that influence forest structure and species distributions over large spatial scales. Here, we employ a novel machine learning framework with high-resolution aerial photos, and LiDAR collected over 115 km2 of El Yunque National Forest in Puerto Rico to examine the effects of topographic exposure to two hurricanes, Hugo (1989) and Georges (1998), and several landscape-scale environmental factors on the current forest height and abundance of a dominant, wind-resistant species, the palm Prestoea acuminata var. montana. Model predictions show that the average density of the palm was 32% greater while the canopy height was 20% shorter in forests exposed to the two storms relative to unexposed areas. Our results demonstrate that hurricanes have lasting effects on forest canopy height and composition, suggesting the expected increase in hurricane severity with a warming climate will alter coastal forests in the North Atlantic.