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The accurate estimation of forest aboveground biomass (AGB) in areas with complex terrain is very important for quantifying the carbon sequestration capacity of forest ecosystems and studying the regional or global carbon cycle. In our previous research, we proposed the radiometric terrain correction (RTC) process for introducing normalized correction factors, which has strong effectiveness and robustness in terms of the backscattering coefficient of polarimetric synthetic aperture radar (PolSAR) data and the monadic model. However, the impact of RTC on the correctness of feature extraction and the performance of regression models requires further exploration in the retrieval of forest AGB based on a machine learning multiple regression model. In this study, based on PolSAR data provided by ALOS-2, 117 feature variables were accurately extracted using the RTC process, and then Boruta and recursive feature elimination with cross-validation (RFECV) algorithms were used to perform multi-step feature selection. Finally, 10 machine learning regression models and the Optuna algorithm were used to evaluate the effectiveness and robustness of RTC in improving the quality of the PolSAR feature set and the performance of the regression models. The results revealed that, compared with the situation without RTC treatment, RTC can effectively and robustly improve the accuracy of PolSAR features (the Pearson correlation R between the PolSAR features and measured forest AGB increased by 0.26 on average) and the performance of regression models (the coefficient of determination R2 increased by 0.14 on average, and the rRMSE decreased by 4.20% on average), but there is a certain degree of overcorrection in the RTC process. In addition, in situations where the data exhibit linear relationships, linear models remain a powerful and practical choice due to their efficient and stable characteristics. For example, the optimal regression model in this study is the Bayesian Ridge linear regression model (R2 = 0.82, rRMSE = 18.06%). [ABSTRACT FROM AUTHOR]

The rapid and accurate estimation of aboveground forest phytomass remains a challenging research task. In general, methods for estimating phytomass fall mainly into the category of field measurements performed by ground-based methods, but approaches based on remote sensing and ecological modelling have been increasingly applied. The aim is to develop the scientific and methodological framework for the remote sensing estimation of qualitative and quantitative characteristics of forest stands, using the combination of surveys and machine learning models to determine phytomass of forest stands and calculate the carbon balance. Even-aged stands of different tree species growing in the forest steppe zone of the East European Plain were chosen as test objects. We have applied the modernized methodological approaches to compare and integrate forest and tree stand characteristics obtained by ground-based and UAV-based comprehensive surveys; additionally, we developed computer vision models and methods for determining the same characteristics by remote sensing methods. The key advantage of the proposed methodology for remote monitoring and carbon balance control over existing analogues is the minimization of the amount of groundwork and, consequently, the reduction inlabor costs without loss of information quality. Reliable data on phytomass volumes will allow for operational control of the forest carbon storage, which is essential for decision-making processes. This is important for the environmental monitoring of forests and green spaces of various economic categories. The proposed methodology is necessary for the monitoring and control of ecological–climatic and anthropogenic–technogenic transformations in various landscapes. The development is useful for organizing the management of ecosystems, environmental protection, and managing the recreational and economic resources of landscapes with natural forests and forest plantations. [ABSTRACT FROM AUTHOR]

Accurate forest biomass estimation serves as the foundation of forest management and holds critical significance for a comprehensive understanding of forest carbon storage and balance. This study aimed to integrate Landsat 8 OLI and Sentinel-1A SAR satellite image data and selected a portion of the Shanxia Experimental Forest in Jiangxi Province as the study area to establish a biomass estimation model by screening influencing factors. Firstly, we extracted spectral information, vegetation indices, principal component features, and texture features within 3 × 3-pixel neighborhoods from Landsat 8 OLI. Moreover, we incorporated Sentinel-1's VV (vertical transmit–vertical receive) and VH (vertical transmit–horizontal receive) polarizations. We proposed an ensemble AGB (aboveground biomass) model based on a neural network. In addition to the neural network model, namely the tent mapping atom search optimized BP neural network (Tent_ASO_BP) model, partial least squares regression (PLSR), support vector machine (SVR), and random forest (RF) regression prediction techniques were also employed to establish the relationship between multisource remote sensing data and forest biomass. Optical variables (Landsat 8 OLI), SAR variables (Sentinel-1A), and their combinations were input into the four prediction models. The results indicate that Tent_ ASO_ BP model can better estimate forest biomass. Compared to pure optical or single microwave data, the Tent_ASO_BP model with the optimal combination of optical and microwave input features achieved the highest accuracy. Its R2 was 0.74, root mean square error (RMSE) was 11.54 Mg/ha, and mean absolute error (MAE) was 9.06 Mg/ha. Following this, the RF model (R2 = 0.54, RMSE = 21.33 Mg/ha, MAE = 17.35 Mg/ha), SVR (R2 = 0.52, RMSE = 17.66 Mg/ha, MAE = 15.11 Mg/ha), and PLSR (R2 = 0.50, RMSE = 16.52 Mg/ha, MAE = 12.15 Mg/ha) models were employed. In conclusion, the BP neural network model improved by tent mapping atom search optimization algorithm significantly enhanced the accuracy of AGB estimation in biomass studies. This will provide a new avenue for large-scale forest resource surveys. [ABSTRACT FROM AUTHOR]

Due to a series of human activities like deforestation and land degradation, the concentration of greenhouse gases has risen significantly. Forest vegetation is an important part of forest ecosystems with high carbon sequestration potential. Estimates of the carbon sequestration rate of forest vegetation in various provinces and districts are helpful to the regional and global Carbon cycle. How to build an effective carbon sequestration potential model and reveal the spatiotemporal evolution trend and driving factors of carbon sequestration potential is an urgent challenge to be solved in carbon cycle simulation and prediction research. This study characterized the carbon sequestration status of forest vegetation using the modified CASA (Carnegie-Ames Stanford Approach) model and estimated the carbon sequestration potential from 2010 to 2060 using the FCS (Forest Carbon Sequestration) model combined with forest age and biomass under the four future Shared Socioeconomic Pathways (SSP) scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, then proposes natural, social, and economic perspectives. This study found that the average NPP of the forest vegetation in Chongqing from 2000 to 2020 was 797.95 g C/m2, and the carbon storage by 2060 was 269.94 Tg C. The carbon sequestration rate varied between <0.01 Tg C/a and 0.20 Tg C/a in various districts and counties. Over time, forest growth gradually slowed, and carbon sequestration rates also decreased. Under the four future climate scenarios, the SSP5-8.5 pathway had the highest carbon sequestration rate. Natural factors had the greatest influence on changes in carbon sequestration rate. This result provides data support and scientific reference for the planning and control of forests and the enhancement of carbon sequestration capacity in Chongqing. [ABSTRACT FROM AUTHOR]

Huge amounts of carbon being sequestered in forest ecosystems make them an important land carbon sink at the global scale. Their ability to withdraw carbon dioxide (CO 2 ) from the atmosphere, whose concentration is gradually increasing due to anthropogenic emissions, renders them important natural climate-mitigation solutions. The urgent need for transition from high to zero net emission on country, continental, and global scales, to slow down the warming to an acceptable level, calls for the analysis of different economic sectors' roles in reaching that ambitious goal. Here, we examine changes in CO 2 emission and sequestration rates during recent decades focusing on the coal-dominated energy sector and Land Use, Land-Use Change, and Forestry (LULUCF) as well as wood production at the country level. The main purpose of the presented study is to examine the potential of storing carbon in standing forest biomass and wood products in Poland as well as the impact of disturbances. The ratio of LULUCF absorption of CO 2 to its emission in Poland has ranged from about 1% in 1992 to over 15% in 2005. From a climate-change mitigation point of view, the main challenge is how to maximize the rate and the duration of CO 2 withdrawal from the atmosphere by its storage in forest biomass and wood products. Enhancing carbon sequestration and storage in forest biomass, via sustainable and smart forestry, is considered to be a nature-based climate solution. However, not only forests but also wood-processing industries should be included as important contributors to climate-change mitigation, since harvested wood products substitute materials like concrete, metal, and plastic, which have a higher carbon footprint. The energy perspective of the paper embraces two aspects. First, CO 2 sequestration in forests and subsequently in harvested wood products, is an effective strategy to offset a part of national CO 2 emissions, resulting largely from fossil fuel burning for energy-production purposes. Second, wood as biomass is a renewable energy source itself, which played an important role in sustaining energy security for many individual citizens of Poland during the unusual conditions of winter 2022/2023, with a scarce coal supply. [ABSTRACT FROM AUTHOR]

Forest ecosystems can be regarded as huge carbon sinks. In order to effectively assess carbon balance in such ecosystems, rapid and accurate estimation of the aboveground biomass of a forest is critically needed. However, the current methods for biomass estimation and mapping are of limited spatial resolution and mostly depend on large numbers of measurements. In order to obtain better biomass estimation outcomes with higher spatial resolution, a rapid method is introduced for region-scale biomass estimation in alpine and canyon areas using space-borne light detection and ranging (LiDAR) data and optical remote-sensing images. Specifically, we explored alpine and canyon areas in Shangri-La City in China using space-borne LiDAR data from ICESAT-2 and optical remote-sensing images from Landsat8 OLI, Sentinel-2, and Microwave remote sensing Sentinel-1. An extrapolation model of the forest canopy heights in these areas was constructed with a 30-m resolution of continuous canopy height outputs. For continuously estimating the diameter at breast height (DBH) in Shangri-La City, a tree height-DBH growth model was constructed based on the LiDAR and remote-sensing measurements. Finally, based on the average DBH of the explored forests, a model was constructed for estimating and mapping the aboveground biomass and carbon storage in Shangri-La with a spatial resolution of 30 m. The results show that the forest canopy height in Shangri-La City is mainly in the range of 2.82–30.96 m, and that the estimation accuracy is verified by the LiDAR-based canopy height model (CHM) with a coefficient of determination of R2 = 0.7143. The inversion results were still largely affected by geospatial location factors (longitude, latitude), terrain factors (slope, elevation), and vegetation indices (NBR, NDGI, NDVI). Based on the relationship between the tree height and the DBH, the DBH of trees in Shangri-La City was estimated to be mainly in the range of 20 cm to 30 cm, and this estimate was verified by actual measurements with R2 greater than 0.7 all. Finally, the established model estimated the aboveground forest biomass and carbon storage of the study area of Shangri-La City in 2020 to be 1.28 × 108 t and 6.41 × 107 t, respectively. These estimates correspond to total accuracies of 92.28%, respectively. [ABSTRACT FROM AUTHOR]

Forests are the main body of carbon sequestration in terrestrial ecosystems and forest aboveground biomass (AGB) is an important manifestation of forest carbon sequestration. Reasonable and accurate quantification of the relationship between AGB and its driving factors is of great importance for increasing the biomass and function of forests. Remote sensing observations and field measurements can be used to estimate AGB in large areas. To explore the applicability of the panel data models in AGB and its driving factors, we compared the results of panel data models (spatial error model and spatial lag model) with those of geographically weighted regression (GWR) and ordinary least squares (OLS) to quantify the relationship between AGB and its driving factors. Furthermore, we estimated the tree height, diameter at breast height, canopy cover (CC) and species diversity index (Shannon–Wiener index) of Robinia pseudoacacia plantations in Changwu on the Loess Plateau using field data and remote sensing images by a random forest model and estimated soil organic carbon (SOC) contents using laboratory data by ordinary kriging (OK) interpolation. We estimated AGB using the already estimated tree height and diameter at breast height combined with the allometric growth equation. In this study, we estimated SOC contents by OK interpolation, and the accuracy R2 values for each soil layer were greater than 0.81. We estimated diameter at breast height (DBH), CC, SW and tree height (TH) using the random forest, and the accuracy R2 values were 0.85, 0.82, 0.76 and 0.68, respectively. We estimated AGB with random forest and the allometric growth equation and found that the average AGB was 55.80 t/ha. The OLS results showed that the residuals of the OLS regression exhibited obvious spatial correlations and rejected OLS applications. GWR, SEM and SLM were used for spatial regression analysis, and SEM was the best model for explaining the relationship between AGB and its driving factors. We also found that AGB was significantly positively correlated with CC, SW, and 0–60 cm SOC content (p < 0.05) and significantly negatively correlated with slope aspect (p < 0.01). This study provides a new idea for studying the relationship between AGB and its driving factors and provides a basis for practical forest management, increasing biomass, and giving full play to the role of carbon sequestration. [ABSTRACT FROM AUTHOR]

Forest above-ground biomass (AGB) is an important index to evaluate forest carbon sequestration capacity, which is very important to maintain the stability of forest ecosystems. At present, the wide use of remote sensing technology makes it possible to estimate the large-scale forest AGB accurately and efficiently. Airborne hyperspectral remote sensing data can obtain rich spectral information and spatial structure information on the forest canopy with the characteristics of high spatial and hyperspectral resolution. Airborne LiDAR data can describe the three-dimensional structure characteristics of a forest and provide vertical structure information related to biomass. Based on the characteristics of the two data sources, this study takes Gaofeng forest farm in Nanning, Guangxi, as the study area, Chinese fir, pine tree, eucalyptus and other broadleaved trees as the research object, and constructs the AGB estimation models of different tree species by fusing airborne LiDAR and hyperspectral features. Firstly, spectral features, texture features, vegetation index, wavelet transform features and edge features are extracted from hyperspectral data. Canopy structure features, point cloud structure features, point cloud density features and terrain features are extracted from airborne LiDAR data. Secondly, the random forest (RF) method is used to screen the features of the two sets of data, and the features with the highest importance are selected. Finally, based on the optimal features of the two data sources, the forest AGB model is constructed using the multiple stepwise regression method. The results show that the texture features extracted by wavelet transform can be used for AGB modeling. The AGB of eucalyptus has high correlation with height features derived from airborne LiDAR, the AGB of other broadleaved trees mostly depends on the wavelet transform texture features from airborne hyperspectral data, while the AGB of Chinese fir and pine tree has high correlation with both height features and spectral features. Feature-fusion-based LiDAR and hyperspectral data can greatly improve the accuracy of the AGB models. The accuracy of the optimal AGB models of Chinese fir, pine tree, eucalyptus and other broadleaved trees is 0.78, 0.95, 0.72 and 0.89, respectively. In conclusion, more accurate estimation results can be obtained by combining active and passive remote sensing data for forest AGB estimation, which provides a solution for carbon storage assessment and forest ecosystem assessment. [ABSTRACT FROM AUTHOR]

The accurate estimation of spatially explicit forest aboveground biomass (AGB) provides an essential basis for sustainable forest management and carbon sequestration accounting, especially in Myanmar, where there is a lack of data for forest conservation due to operational limitations. This study mapped the forest AGB using Sentinel-2 (S-2) images and Shuttle Radar Topographic Mission (SRTM) based on random forest (RF), stochastic gradient boosting (SGB) and Kriging algorithms in two forest reserves (Namhton and Yinmar) in Myanmar, and compared their performance against AGB measured by the traditional methods. Specifically, a suite of forest sample plots were deployed in the two forest reserves, and forest attributes were measured to calculate the plot-level AGB based on allometric equations. The spectral bands, vegetation indices (VIs) and textures derived from processed S-2 data and topographic parameters from SRTM were utilized to statistically link with field-based AGB by implementing random forest (RF) and stochastic gradient boosting (SGB) algorithms. Followed by an evaluation of the algorithmic performances, RF-based Kriging (RFK) models were employed to determine the spatial distribution of AGB as an improvement of accuracy against RF models. The study's results showed that textural measures produced from wavelet analysis (WA) and vegetation indices (VIs) from Sentinel-2 were the strongest predictors for evergreen forest reserve (Namhton) AGB prediction and spectral bands and vegetation indices (VIs) showed the highest sensitivity to the deciduous forest reserve (Yinmar) AGB prediction. The fitted models were RF-based ordinary Kriging (RFOK) for Namhton forest reserve and RF-based co-Kriging (RFCK) for Yinmar forest reserve because their respective R2, whilst the RMSE values were validated as 0.47 and 24.91 AGB t/ha and 0.52 and 34.72 AGB t/ha, respectively. The proposed random forest Kriging framework provides robust AGB maps, which are essential to estimate the carbon sequestration potential in the context of REDD+. From this particular study, we suggest that the protection/disturbance status of forests affects AGB values directly in the study area; thus, community-participated or engaged forest utilization and conservation initiatives are recommended to promote sustainable forest management. [ABSTRACT FROM AUTHOR]

The management of low-density savannah and woodland forests for carbon storage presents a mechanism to offset the expense of ecologically informed forest management strategies. However, existing carbon monitoring systems draw on vast amounts of either field observations or aerial light detection and ranging (LiDAR) collections, making them financially prohibitive in low productivity systems where forest management focuses on promoting resilience to disturbance and multiple uses. This study evaluates how UAS altitude and flight speed influence area-based aboveground forest biomass model predictions. The imagery was acquired across a range of UAS altitudes and flight speeds that influence the efficiency of data collection. Data were processed using common structures from motion photogrammetry algorithms and then modeled using Random Forest. These results are compared to LiDAR observations collected from fixed-wing manned aircraft and modeled using the same routine. Results show a strong positive relationship between flight altitude and plot-based aboveground biomass modeling accuracy. UAS predictions increasingly outperformed (2–24% increased variance explained) commercial airborne LiDAR strategies as acquisition altitude increased from 80–120 m. The reduced cost of UAS data collection and processing and improved biomass modeling accuracy over airborne LiDAR approaches could make carbon monitoring viable in low productivity forest systems. [ABSTRACT FROM AUTHOR]

Mapping and monitoring of forest carbon stocks across large areas in the tropics will necessarily rely on remote sensing approaches, which in turn depend on field estimates of biomass for calibration and validation purposes. Here, we used field plot data collected in a tropical moist forest in the central Amazon to gain a better understanding of the uncertainty associated with plot-level biomass estimates obtained specifically for the calibration of remote sensing measurements. In addition to accounting for sources of error that would be normally expected in conventional biomass estimates (e.g., measurement and allometric errors), we examined two sources of uncertainty that are specific to the calibration process and should be taken into account in most remote sensing studies: the error resulting from spatial disagreement between field and remote sensing measurements (i.e., co-location error), and the error introduced when accounting for temporal differences in data acquisition. We found that the overall uncertainty in the field biomass was typically 25% for both secondary and primary forests, but ranged from 16 to 53%. Co-location and temporal errors accounted for a large fraction of the total variance (>65%) and were identified as important targets for reducing uncertainty in studies relating tropical forest biomass to remotely sensed data. Although measurement and allometric errors were relatively unimportant when considered alone, combined they accounted for roughly 30% of the total variance on average and should not be ignored. Our results suggest that a thorough understanding of the sources of error associated with field-measured plot-level biomass estimates in tropical forests is critical to determine confidence in remote sensing estimates of carbon stocks and fluxes, and to develop strategies for reducing the overall uncertainty of remote sensing approaches. [ABSTRACT FROM AUTHOR]

The scientific community involved in the UN-REDD program is still reporting large uncertainties about the amount and spatial variability of CO2 stored in forests. The main limitation has been the lack of field samplings over space and time needed to calibrate and convert remote sensing measurements into aboveground biomass (AGB). As an alternative to costly field inventories, we examine the reliability of state-of-the-art lidar methods to provide direct retrieval of many forest metrics that are commonly collected through field sampling techniques (e.g., tree density, individual tree height, crown cover). AGB is estimated using existing allometric equations that are fed by lidar-derived metrics at either the individual tree- or forest layer-level (for the overstory or underneath layers, respectively). Results over 40 plots of a multilayered forest located in northwest Portugal show that the lidar method provides AGB estimates with a relatively small random error (RMSE = of 17.1%) and bias (of 4.6%). It provides local AGB baselines that meet the requirements in terms of accuracy to calibrate satellite remote sensing measurements (e.g., the upcoming lidar GEDI (Global Ecosystem Dynamics Investigation), and the Synthetic Aperture Radar (SAR) missions NISAR (National Aeronautics and Space Administration and Indian Space Research Organization SAR) and BIOMASS from the European Space Agency, ESA) for AGB mapping purposes. The development of similar techniques over a variety of forest types would be a significant improvement in quantifying CO2 stocks and changes to comply with the UN-REDD policies. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) can provide new ways to measure forests and supplement expensive or labor-intensive inventory methods. Forest carbon, a key uncertainty in the global carbon cycle and also important for carbon conservation programs, is typically monitored using manned aircraft or extensive forest plot networks to estimate aboveground carbon density (ACD). Manned aircraft are only cost-effective when applied to large areas (>100,000 ha), while plot networks are most effective for total C stock estimation across large areas, not for quantifying spatially-explicit variation. We sought to develop an effective method for frequent and accurate ACD estimation at intermediate scales (100-100,000 ha) that would be sensitive to small-scale disturbance. Using small UAVs, we collected imagery of 516 ha of lowland forest in the Peruvian Amazon. We then used a structure-from-motion (SFM) approach to create a 3D model of forest canopy. Comparing SFM- and airborne Light Detection and Ranging (LiDAR)-derived estimates of canopy height and ACD, we found that SFM estimates of top-of-canopy height (TCH) and ACD were highly correlated with previous LiDAR estimates (r = 0.86-0.93 and r = 0.73-0.94 for TCH and ACD, respectively, at 0.1-4 ha grain sizes), with r = 0.92 for ACD determination at the 1 ha scale, despite SFM and LiDAR measurements being separated by two years in a dynamic forest. SFM and LiDAR estimates of mean TCH and mean ACD were highly similar, differing by only 0.4% and 0.04%, respectively, within mature forest. The technique allows inexpensive, near-real-time monitoring of ACD for ecological studies, payment for ecosystem services (PES) ventures, such as reducing emissions from deforestation and forest degradation (REDD+), forestry enterprises, and governance. [ABSTRACT FROM AUTHOR]

The Tumen River Basin, located in the cross-border region of China, North Korea, and Russia, constitutes an important ecological barrier in China. Forest here is mainly distributed around wetland, with the distribution of wetland having the potential to regulate regional forest carbon storage. However, the spatially explicit map of forest aboveground biomass (AGB) and potential impact of drivers, i.e., wetland distribution and climate, is still lacking. We thus use a deep neural network and multi-source remote sensing data to quantify forest AGB in the Tumen River Basin. Our results show the mean forest AGB is 103.43 Mg ha−1, with divergent spatial variation along its distance to wetland. The results of correlation analysis showed that with sufficient soil moisture supply, temperature dominant spatial variation of forest aboveground biomass. Noted that using the space for time substitution, we find when wetland decreased by less than 11.1%, the forest AGB decreased by more than 8%. Our result highlight the signatures of wetland impact on its nearby forest carbon storage, and urge the wetland protection, especially under the warming and drying future. [ABSTRACT FROM AUTHOR]

Tropical forests are acknowledged for providing important ecosystem services and are renowned as "the lungs of the planet Earth" due to their role in the exchange of gasses—particularly inhaling CO2 and breathing out O2—within the atmosphere. Overall, the forests provide 50% of the total plant biomass of the Earth, which accounts for 450–650 PgC globally. Understanding and accurate estimates of tropical forest biomass stocks are imperative in ascertaining the contribution of the tropical forests in global carbon dynamics. This article provides a review of remote-sensing-based approaches for the assessment of above-ground biomass (AGB) across the tropical forests (global to national scales), summarizes the current estimate of pan-tropical AGB, and discusses major advancements in remote-sensing-based approaches for AGB mapping. The review is based on the journal papers, books and internet resources during the 1980s to 2020. Over the past 10 years, a myriad of research has been carried out to develop methods of estimating AGB by integrating different remote sensing datasets at varying spatial scales. Relationships of biomass with canopy height and other structural attributes have developed a new paradigm of pan-tropical or global AGB estimation from space-borne satellite remote sensing. Uncertainties in mapping tropical forest cover and/or forest cover change are related to spatial resolution; definition adapted for 'forest' classification; the frequency of available images; cloud covers; time steps used to map forest cover change and post-deforestation land cover land use (LCLU)-type mapping. The integration of products derived from recent Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LiDAR) satellite missions with conventional optical satellite images has strong potential to overcome most of these uncertainties for recent or future biomass estimates. However, it will remain a challenging task to map reference biomass stock in the 1980s and 1990s and consequently to accurately quantify the loss or gain in forest cover over the periods. Aside from these limitations, the estimation of biomass and carbon balance can be enhanced by taking account of post-deforestation forest recovery and LCLU type; land-use history; diversity of forest being recovered; variations in physical attributes of plants (e.g., tree height; diameter; and canopy spread); environmental constraints; abundance and mortalities of trees; and the age of secondary forests. New methods should consider peak carbon sink time while developing carbon sequestration models for intact or old-growth tropical forests as well as the carbon sequestration capacity of recovering forest with varying levels of floristic diversity. [ABSTRACT FROM AUTHOR]

The characterization of carbon stocks and dynamics at the national level is critical for countries engaging in climate change mitigation and adaptation strategies. However, several tropical countries, including Kenya, lack the essential information typically provided by a complete national forest inventory. Here we present the most detailed and rigorous national-scale assessment of aboveground woody biomass carbon stocks and dynamics for Kenya to date. A non-parametric random forest algorithm was trained to retrieve aboveground woody biomass carbon (AGBC) for the year 2014 ± 1 and forest disturbances for the 2014–2017 period using in situ forest inventory plot data and satellite Earth Observation (EO) data. The ecosystem carbon cycling of Kenya's forests and wooded grassland were assessed using a model-data fusion framework, CARDAMOM, constrained by the woody biomass datasets from this study as well as time series information on leaf area, fire events and soil organic carbon. Our EO-derived AGBC stocks were estimated as 140 Mt C for forests and 199 Mt C for wooded grasslands. The total AGBC loss during the study period was estimated as 1.89 Mt C with a dispersion below 1%. The CARDAMOM analysis estimated woody productivity to be three times larger in forests (mean = 1.9 t C ha−1 yr−1) than wooded grasslands (0.6 t C ha−1 yr−1), and the mean residence time of woody C in forests (16 years) to be greater than in wooded grasslands (10 years). This study stresses the importance of carbon sequestration by forests in the international climate mitigation efforts under the Paris Agreement, but emphasizes the need to include non-forest ecosystems such as wooded grasslands in international greenhouse gas accounting frameworks. [ABSTRACT FROM AUTHOR]

Quantifying spatially explicit or pixel-level aboveground forest biomass (AFB) across large regions is critical for measuring forest carbon sequestration capacity, assessing forest carbon balance, and revealing changes in the structure and function of forest ecosystems. When AFB is measured at the species level using widely available remote sensing data, regional changes in forest composition can readily be monitored. In this study, wall-to-wall maps of species-level AFB were generated for forests in Northeast China by integrating forest inventory data with Moderate Resolution Imaging Spectroradiometer (MODIS) images and environmental variables through applying the optimal k-nearest neighbor (kNN) imputation model. By comparing the prediction accuracy of 630 kNN models, we found that the models with random forest (RF) as the distance metric showed the highest accuracy. Compared to the use of single-month MODIS data for September, there was no appreciable improvement for the estimation accuracy of species-level AFB by using multi-month MODIS data. When k > 7, the accuracy improvement of the RF-based kNN models using the single MODIS predictors for September was essentially negligible. Therefore, the kNN model using the RF distance metric, single-month (September) MODIS predictors and k = 7 was the optimal model to impute the species-level AFB for entire Northeast China. Our imputation results showed that average AFB of all species over Northeast China was 101.98 Mg/ha around 2000. Among 17 widespread species, larch was most dominant, with the largest AFB (20.88 Mg/ha), followed by white birch (13.84 Mg/ha). Amur corktree and willow had low AFB (0.91 and 0.96 Mg/ha, respectively). Environmental variables (e.g., climate and topography) had strong relationships with species-level AFB. By integrating forest inventory data and remote sensing data with complete spatial coverage using the optimal kNN model, we successfully mapped the AFB distribution of the 17 tree species over Northeast China. We also evaluated the accuracy of AFB at different spatial scales. The AFB estimation accuracy significantly improved from stand level up to the ecotype level, indicating that the AFB maps generated from this study are more suitable to apply to forest ecosystem models (e.g., LINKAGES) which require species-level attributes at the ecotype scale. [ABSTRACT FROM AUTHOR]

Urban population has grown exponentially in recent years, leading to an increase of CO2 emissions and consequently contributing on a large scale to climate change. Urban trees are fundamental to mitigating CO2 emissions as they incorporate carbon in their biomass. It becomes necessary to understand and measure urban tree carbon storage. In this paper is studied the potential of open data to measure the quantity, density, and value of carbon stored by the seven most represented urban trees in the city of Lisbon. To compute carbon storage, the seven most represented urban tree species were selected from an open database acquired from an open data portal of the city of Lisbon. Through allometric equations, it was possible to compute the trees' biomass and calculate carbon storage quantity, density, and value. The results showed that the tree species Celtis australis is the species that contributes more to carbon storage. Central parishes of the city of Lisbon present higher-density values of carbon storage when compared with the border parishes despite the first ones presenting low-to-medium values of carbon storage quantity and value. Trees located in streets, present higher values of carbon storage, when compared with trees located in schools and green areas. Finally, the potential usage of this information to build a decision-support dashboard for planning green infrastructures was demonstrated. [ABSTRACT FROM AUTHOR]