Global Forest Biomass Research Articles

Climate policies for carbon neutrality should not rely on the uncertain increase of carbon stocks in existing forests

The international community, through treaties such as the Paris agreement, aims to limit climate change to well below 2 °C, which implies reaching carbon neutrality around the second half of the century. In the current calculations underpinning the various roadmaps toward carbon neutrality, a major component is a steady or even expanding terrestrial carbon sink, supported by an increase of global forest biomass. However, recent research has challenged this view. Here we developed a framework that assesses the potential global equilibrium of forest biomass under different climate change scenarios. Results show that under global warming carbon storage potential in forest aboveground biomass gradually shifts to higher latitudes and the intensity of the disturbance regimes increases significantly almost everywhere. CO2 fertilization stands out as the most uncertain process, with different methods of estimation leading to diverging results by almost 155 PgC of above ground biomass at equilibrium. Overall, assuming that the sum of human pressures (e.g. wood extraction) does not change over time, that total forest cover does not change significantly and that the trend in CO2 fertilisation as it is currently estimated from satellite proxy observations remains, results show that we have reached (or are very close to reaching) the peak of global forest carbon storage. In the short term, where increased disturbance regimes are assumed to act quicker than increased forest growth potential, global forests might instead act as a carbon source, that will require even more effort in decarbonization than previously estimated. Therefore, the potential of forests as a nature-based solution to mitigate climate change brings higher uncertainties and risks than previously thought.

Global Forest Plantations Mapping and Biomass Carbon Estimation

AbstractManagement of forest plantations is a natural based solution to the global‐scale mitigation of climate change; however, the role of carbon sequestration remains poorly understood, and this is hampered by a lack of detailed distribution on the global forest plantations. For the first time, we generated a global spatial distribution for forest plantations (GSDFP) during 2015 at a spatial resolution of 250 m using hierarchical extraction based on a machine learning algorithm taking time series of MODIS and ALOS PALSAR imagery as the source data, and finally estimates the biomass carbon stored in forest plantations. The resultant map was validated using reference samples visually interpreted, as well as reference samples collected from previous studies, and inter‐compared with statistical forest plantation area and regional forest plantation maps, and the GSDFP was found to accurately represent the spatial distribution of forest plantations around the globe. Globally, forest plantations account for 7.35% (360.22 × 104 km2) of the global forest area and are estimated to contribute 4.60% (12.85 Pg C) of global forest biomass carbon. The forest plantation map and biomass carbon stock estimates will assist forest management and provide a benchmark for the estimation of the forest plantation carbon sink.

Awareness of Bartın High School Students on Global Climate Change and Forest Biomass

Toplumların özellikle çalışmanın kitlesini oluşturan lise öğrencilerinin eğitim düzeylerinin iyileştirilebilmesinin ilk aşaması, konu hakkındaki bilinç düzeylerinin belirlenmesidir. Bartın il merkezinde lise seviyesinde eğitim gören öğrencilerin küresel iklim değişikliği ve iklim değişikliği ile mücadelede önemli olan orman biyokütlesi hakkında farkındalık seviyelerini belirlemek, çalışmanın ana amacıdır. Bu amaçla, 525 lise öğrencisi üzerinde anket çalışması uygulanmıştır. Anket sonuçları cinsiyet, okul türleri ve küresel iklim değişikliği konularında eğitim almış olma durumlarına göre incelenmiştir. Çalışma sonucunda genel olarak kadın öğrencilerin erkek öğrencilere, eğitim alan öğrencilerin eğitim almayan öğrencilere kıyasla algılarının daha yüksek olduğu belirlenmiştir. Buna ilaveten Bartın ili genelinde Mesleki ve Teknik liseleride eğitim gören öğrencilerin algılarının en düşük seviyede olduğu belirlenmiştir. Öğrenciler tarafından %58,5 oranıyla Ormanların azalması ve ekolojik dengenin bozulması en önemli ikinci sorun olarak nitelendirilirken, orman biyokütlesi %58,7; karbon havuzu %59 oranıyla doğru tanımlanmıştır. Seragazı tanımı ise %75,2 olarak doğru bilinirken kız öğrenciler erkek öğrencilerden daha yüksek algıya sahip oldukları belirlenmiştir (t [496] =-2,000; p

Characterizing aboveground biomass and tree cover of regrowing forests in Brazil using multi‐source remote sensing data

AbstractCharacterization of regrowing forests is vital for understanding forest dynamics to assess the impacts on carbon stocks and to support sustainable forest management. Although remote sensing is a key tool for understanding and monitoring forest dynamics, the use of exclusively remotely sensed data to explore the effects of different variables on regrowing forests across all biomes in Brazil has rarely been investigated. Here, we analyzed how environmental and human factors affect regrowing forests. Based on Brazil's secondary forest age map, 3060 locations disturbed between 1984 and 2018 were sampled, interpreted and analyzed in different biomes. We interpreted the time since disturbance for the sampled pixels in Google Earth Engine. Elevation, slope, climatic water deficit (CWD), the total Nitrogen of soil, cation exchange capacity (CEC) of soil, surrounding tree cover, distance to roads, distance to settlements and fire frequency were analyzed in their importance for predicting aboveground biomass (AGB) and tree cover derived from global forest aboveground biomass map and tree cover map, respectively. Results show that time since disturbance interpreted from satellite time series is the most important predictor for characterizing AGB and tree cover of regrowing forests. AGB increased with increasing time since disturbance, surrounding tree cover, soil total N, slope, distance to roads, distance to settlements and decreased with larger fire frequency, CWD and CEC of soil. Tree cover increased with larger time since disturbance, soil total N, surrounding tree cover, distance to roads, distance to settlements, slope and decreased with increasing elevation and CWD. These results emphasize the importance of remotely sensing products as key opportunities to improve the characterization of forest regrowth and to reduce data gaps and uncertainties related to forest carbon sink estimation. Our results provide a better understanding of regional forest dynamics, toward developing and assessing effective forest‐related restoration and climatic mitigation strategies.

Exploring characteristics of national forest inventories for integration with global space-based forest biomass data

National forest inventories (NFIs) are a reliable source for national forest measurements. However, they are usually not developed for linking with remotely sensed (RS) biomass information. There are increasing needs and opportunities to facilitate this link towards better global and national biomass estimation. Thus, it is important to study and understand NFI characteristics relating to their integration with space-based products; in particular for the tropics where NFIs are quite recent, less frequent, and partially incomplete in several countries. Here, we (1) assessed NFIs in terms of their availability, temporal distribution, and extent in 236 countries from FAO's Global Forest Resources Assessment (FRA) 2020; (2) compared national forest biomass estimates in 2018 from FRA and global space-based Climate Change Initiative (CCI) product in 182 countries considering NFI availability and temporality; and (3) analyzed the latest NFI design characteristics in 46 tropical countries relating to their integration with space-based biomass datasets. We observed significant NFI availability globally and multiple NFIs were mostly found in temperate and boreal countries while most of the single NFI countries (94 %) were in the tropics. The latest NFIs were more recent in the tropics and many countries (35) implemented NFIs from 2016 onwards. The increasing availability and update of NFIs create new opportunities for integration with space-based data at the national level. This is supported by the agreement we found between country biomass estimates for 2018 from FRA and CCI product, with a significantly higher correlation in countries with recent NFIs. We observed that NFI designs varied greatly in tropical countries. For example, the size of the plots ranged from 0.01 to 1 ha and more than three-quarters of the countries had smaller plots of ≤0.25 ha. The existing NFI designs could pose specific challenges for statistical integration with RS data in the tropics. Future NFI and space-based efforts should aim towards a more integrated approach taking advantage of both data streams to improve national estimates and help future data harmonization efforts. Regular NFI efforts can be expanded with the inclusion of some super-site plots to enhance data integration with currently available space-based applications. Issues related to cost implications versus improvements in the accuracy, timeliness, and sustainability of national forest biomass estimation should be further explored.

Altered growth conditions more than reforestation counteracted forest biomass carbon emissions 1990\u20132020

Understanding the carbon (C) balance in global forest is key for climate-change mitigation. However, land use and environmental drivers affecting global forest C fluxes remain poorly quantified. Here we show, following a counterfactual modelling approach based on global Forest Resource Assessments, that in 1990–2020 deforestation is the main driver of forest C emissions, partly counteracted by increased forest growth rates under altered conditions: In the hypothetical absence of changes in forest (i) area, (ii) harvest or (iii) burnt area, global forest biomass would reverse from an actual cumulative net C source of c. 0.74 GtC to a net C sink of 26.9, 4.9 and 0.63 GtC, respectively. In contrast, (iv) without growth rate changes, cumulative emissions would be 7.4 GtC, i.e., 10 times higher. Because this sink function may be discontinued in the future due to climate-change, ending deforestation and lowering wood harvest emerge here as key climate-change mitigation strategies.

The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations

Abstract. The terrestrial forest carbon pool is poorly quantified, in particular in regions with low forest inventory capacity. By combining multiple satellite observations of synthetic aperture radar (SAR) backscatter around the year 2010, we generated a global, spatially explicit dataset of above-ground live biomass (AGB; dry mass) stored in forests with a spatial resolution of 1 ha. Using an extensive database of 110 897 AGB measurements from field inventory plots, we show that the spatial patterns and magnitude of AGB are well captured in our map with the exception of regional uncertainties in high-carbon-stock forests with AGB >250 Mg ha−1, where the retrieval was effectively based on a single radar observation. With a total global AGB of 522 Pg, our estimate of the terrestrial biomass pool in forests is lower than most estimates published in the literature (426–571 Pg). Nonetheless, our dataset increases knowledge on the spatial distribution of AGB compared to the Global Forest Resources Assessment (FRA) by the Food and Agriculture Organization (FAO) and highlights the impact of a country's national inventory capacity on the accuracy of the biomass statistics reported to the FRA. We also reassessed previous remote sensing AGB maps and identified major biases compared to inventory data, up to 120 % of the inventory value in dry tropical forests, in the subtropics and temperate zone. Because of the high level of detail and the overall reliability of the AGB spatial patterns, our global dataset of AGB is likely to have significant impacts on climate, carbon, and socio-economic modelling schemes and provides a crucial baseline in future carbon stock change estimates. The dataset is available at https://doi.org/10.1594/PANGAEA.894711 (Santoro, 2018).

Regionalization of an Existing Global Forest Canopy Height Model for Forests of the Southern United States

A large-scale global canopy height map (GCHM) is essential for global forest aboveground biomass estimation. Four GCHMs have recently been built using data from the Geoscience Laser Altimeter System (GLAS) sensor aboard the Ice, Cloud, and land Elevation Satellite (ICESat), along with auxiliary spatial and climate information. The main objectives of this research were to find out how well a selected GCHM agrees with airborne lidar data from locations in the southern United States and to recalibrate that GCHM to more closely match the forest canopy heights found in the region. The airborne lidar resource was built from data collected between 2010 and 2012, available from in-house and publicly available sources, for sites that included a variety of vegetation types across the southern United States. EPA ecoregions were used to provide ecosystem information for the southern United States. The airborne lidar data were pre-processed to provide lidar-derived metrics, and assigned to four height categories—namely, returns from above 0 m, 1 m, 3 m, and 5 m. The assessment phase results indicated that the 90th and 95th percentiles of the airborne lidar height values were well-suited for use in the recalibration phase of the study. Simple linear regression was used to generate a new, recalibrated GCHM. It was concluded that the characterization of the agreement of a selected GCHM with local data, followed by the recalibration of the existing GCHM to the local region, is both viable and essential for future GCHMs in studies conducted at large scales.

Peatland protection and restoration are key for climate change mitigation

Peatlands cover only about 3% the global land area, but store about twice as much carbon as global forest biomass. If intact peatlands are drained for agriculture or other human uses, peat oxidation can result in considerable CO2 emissions and other greenhouse gases (GHG) for decades or even centuries. Despite their importance, emissions from degraded peatlands have so far not been included explicitly in mitigation pathways compatible with the Paris Agreement. Such pathways include land-demanding mitigation options like bioenergy or afforestation with substantial consequences for the land system. Therefore, besides GHG emissions owing to the historic conversion of intact peatlands, the increased demand for land in current mitigation pathways could result in drainage of presently intact peatlands, e.g. for bioenergy production. Here, we present the first quantitative model-based projections of future peatland dynamics and associated GHG emissions in the context of a 2 °C mitigation pathway. Our spatially explicit land-use modelling approach with global coverage simultaneously accounts for future food demand, based on population and income projections, and land-based mitigation measures. Without dedicated peatland policy and even in the case of peatland protection, our results indicate that the land system would remain a net source of CO2 throughout the 21st century. This result is in contrast to the outcome of current mitigation pathways, in which the land system turns into a net carbon sink by 2100. However, our results indicate that it is possible to reconcile land use and GHG emissions in mitigation pathways through a peatland protection and restoration policy. According to our results, the land system would turn into a global net carbon sink by 2100, as projected by current mitigation pathways, if about 60% of present-day degraded peatlands would be rewetted in the coming decades, next to the protection of intact peatlands.

Fusion of Multiple Gridded Biomass Datasets for Generating a Global Forest Aboveground Biomass Map

Many advanced satellite estimation methods have been developed, but global forest aboveground biomass (AGB) products remain largely uncertain. In this study, we explored data fusion techniques to generate a global forest AGB map for the 2000s at 0.01-degree resolution with improved accuracy by integrating ten existing local or global maps. The error removal and simple averaging algorithm, which is efficient and makes no assumption about the data and associated errors, was proposed to integrate these ten forest AGB maps. We first compiled the global reference AGB from in situ measurements and high-resolution AGB data that were originally derived from field data and airborne lidar data and determined the errors of each forest AGB map at the pixels with corresponding reference AGB values. Based on the errors determined from reference AGB data, the pixel-by-pixel errors associated with each of the ten AGB datasets were estimated from multiple predictors (e.g., leaf area index, forest canopy height, forest cover, land surface elevation, slope, temperature, and precipitation) using the random forest algorithm. The estimated pixel-by-pixel errors were then removed from the corresponding forest AGB datasets, and finally, global forest AGB maps were generated by combining the calibrated existing forest AGB datasets using the simple averaging algorithm. Cross-validation using reference AGB data showed that the accuracy of the fused global forest AGB map had an R-squared of 0.61 and a root mean square error (RMSE) of 53.68 Mg/ha, which is better than the reported accuracies (R-squared of 0.56 and RMSE larger than 80 Mg/ha) in the literature. Intercomparison with previous studies also suggested that the fused AGB estimates were much closer to the reference AGB values. This study attempted to integrate existing forest AGB datasets for generating a global forest AGB map with better accuracy and moved one step forward for our understanding of the global terrestrial carbon cycle by providing improved benchmarks of global forest carbon stocks.

Use of local and global maps of forest canopy height and aboveground biomass to enhance local estimates of biomass in miombo woodlands in Tanzania

Field surveys are often a primary source of aboveground biomass (AGB) data, but plot-based estimates of parameters related to AGB are often not sufficiently precise, particularly not in tropical countries. Remotely sensed data may complement field data and thus help to increase the precision of estimates and circumvent some of the problems with missing sample observations in inaccessible areas. Here, we report the results of a study conducted in a 15,867 km² area in the dry miombo woodlands of Tanzania, to quantify the contribution of existing canopy height and biomass maps to improving the precision of canopy height and AGB estimates locally. A local and a global height map and three global biomass maps, and a probability sample of 513 inventory plots were subject to analysis. Model-assisted sampling estimators were used to estimate mean height and AGB across the study area using the original maps and then with the maps calibrated with local inventory plots. Large systematic map errors – positive or negative – were found for all the maps, with systematic errors as great as 60–70 %. The maps contributed nothing or even negatively to the precision of mean height and mean AGB estimates. However, after being calibrated locally, the maps contributed substantially to increasing the precision of both mean height and mean AGB estimates, with relative efficiencies (variance of the field-based estimates relative to the variance of the map-assisted estimates) of 1.3–2.7 for the overall estimates. The study, although focused on a relatively small area of dry tropical forests, illustrates the potential strengths and weaknesses of existing global forest height and biomass maps based on remotely sensed data and universal prediction models. Our results suggest that the use of regional or local inventory data for calibration can substantially increase the precision of map-based estimates and their applications in assessing forest carbon stocks for emission reduction programs and policy and financial decisions.

A New Method for Generating a Global Forest Aboveground Biomass Map From Multiple High-Level Satellite Products and Ancillary Information

Global forest aboveground biomass (AGB) is very important in quantifying carbon stock, and, therefore, it is necessary to estimate forest AGB accurately. Many studies have obtained reliable AGB estimates by using light detection and ranging (LiDAR) data. However, it is difficult to obtain LiDAR data continuously at regional or global scale. Although many studies have integrated multisource data to estimate biomass to compensate for these deficiencies, few methods can be applied to produce global time series of high-resolution AGB due to the complexity of the method, data source limitations, and large uncertainty. This study developed a new method to produce a global forest AGB map using multiple data sources-including LiDAR-derived biomass products, a suite of high-level satellite products, forest inventory data, and other auxiliary datasets-to train estimated models for five different forest types. We explored three machine learning methods [artificial neural network, multivariate adaptive regression splines, and gradient boosting regression tree (GBRT)] to build the estimated models. The GBRT method was the optimal algorithm for generating a global forest AGB map at a spatial resolution of 1 km. The independent validation result showed good accuracy with an R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value of 0.90 and a root mean square error value of 35.87 Mg/ha. Moreover, we compared the generated global forest AGB map with several other forest AGB maps and found the results to be highly consistent. An important feature of this new method is its ability to produce time series of high-resolution global forest AGB maps because it heavily relies on high-level satellite products.

Stand carbon density drivers and changes under future climate scenarios across global forests

Climate and soil factors drive forest carbon (C) density. However, their impacts on the aboveground carbon density (ACD) and belowground carbon density (BCD) of global forest types have not been quantified in previous studies. In this study, we compiled global forest biomass data that included 8800 plots to better quantify the changes in total carbon density (TCD) of five forest types under current climate and future climate change scenarios (e.g., RCP2.6, RCP4.5 and RCP8.5) using the boosted regression tree method and TCD-stand age-climate models. Tropical forests have the largest amount of C density (130.0 ± 3.2 Mg C ha−1; 173.60 ± 17.24 Mg C ha−1), while boreal forests have the lowest TCDs (59.4 ± 0.9 Mg C ha−1; 62.34 ± 15.10 Mg C ha−1) in the current and future. For the current forest scenarios, the dryness index (DI) for tropical forests exhibited a negative mean change rate for the ACD (4.5 Mg C ha−1/0.1 DI) and BCD (0.9 Mg C ha−1/0.1 DI). The mean annual precipitation (MAP) had a positive mean change rate for the ACD (7.1 Mg C ha−1/100 mm) and BCD (1.3 Mg C ha−1/100 mm) when the MAP was ≤1100 mm in the temperate forests. The MAP was negative for the ACD (1.9 Mg C ha−1/100 mm) and BCD (0.4 Mg C ha−1/100 mm) when the MAP was >1100 mm. The mean annual temperature (MAT) in boreal forests had a positive mean change rate for the ACD (1.78 Mg C ha−1/1°C) and BCD (0.4 Mg C ha−1/1°C). We found that in contrast to 2050, there were positive impacts of the DI on the TCD change in tropical forests in 2070, and the DI was the most important driver for tropical forests. There were strong positive MAP impacts on the TCD change in temperate forests in both 2050 and 2070, where the MAP was the most important driver for temperate forests. There were negative MAT impacts on the TCD change in boreal forests under RCP4.5 (−0.22 ± 10.53 Mg C ha−1) and RCP8.5 (−4.75 ± 10.80 Mg C ha−1) in 2070, and the MAT was the most important driver in boreal forests. Our results could be used to improve the accuracy of C density estimates and assess climate change impacts on the C budgets of global forests.

Forest Height Mapping Using Complex-Valued Convolutional Neural Network

Global forest height and biomass mapping is an important issue for ecosystem studies. Polarimetric synthetic aperture radar (SAR) interferometry (PolInSAR) is an attractive technique for extracting forest parameters by inverting the physical scattering model. However, the simplified scattering model restricts the inversion accuracy unless multi-baseline measurements are adopted, and this will lead to increasing cost. In this paper, a complex-valued convolutional neural network (CV-CNN) model using PolInSAR features of single baseline is proposed for forest height mapping with high accuracy. The supervised learning process shows that the obtained CV-CNN model is accurate enough to describe the complicated forest scattering process. Both simulations and experiments on airborne E-SAR data set demonstrate that the proposed CV-CNN model-based method can greatly improve the forest height inversion accuracy. Experimental results show that the coefficient of determination (r2) increases from 0.83 to 0.92, while root-mean-square error (RMSE) decreases from 3.74m to 2.58m. This promising approach makes it possible to map forest heights more accurately from the single-baseline PolInSAR observations, which will further promote the wide use of PolInSAR technique.

Mapping global forest biomass and its changes over the first decade of the 21st century

Forests played an important role in carbon sequestration during the past two decades. Using a model tree ensemble method (MTE) to regress the seven reflectance bands of EOS-Terra-MODIS satellite data against country level forest biomass carbon density (BCD) of 2001–2005 provided by United Nations’s Forest Resource Assessment (FRA), we developed a global map of forest BCD at 1 km ×1 km resolution for both 2001–2005 and 2006–2010. For 2006–2010, the total global forest biomass carbon stock is estimated as 279.6±7.1 Pg C, and the tropical forest biomass carbon stock is estimated as 174.4±5.4 Pg C. During the first decade of the 21st century, we estimated an increase of global forest biomass of 0.28±0.75 Pg C yr−1. Tropical forest biomass carbon stock slightly decreased (−0.31±0.60 Pg C yr−1); by contrast, temperate and boreal forest biomass increased (0.58±0.28 Pg C yr−1) during the same period. Our estimation of the global forest biomass carbon stock and its changes is subject to uncertainties due to lack of extensive ground measurements in the tropics, spatial heterogeneity in large countries, and different definitions of forest. The continuously monitoring of forest biomass carbon stock with MODIS satellite data will provide useful information for detecting forest changes.

Improved forest height estimation by fusion of simulated GEDI Lidar data and TanDEM-X InSAR data

Interferometric Synthetic Aperture Radar (InSAR) and lidar are increasingly used active remote sensing techniques for forest structure observation. The TanDEM-X (TDX) InSAR mission of German Aerospace Center (DLR) and the upcoming Global Ecosystem Dynamics Investigation (GEDI) of National Aeronautics and Space Administration (NASA) together may provide more accurate estimates of global forest structure and biomass via their synergic use. In this paper, we explored the efficacy of simulated GEDI data in improving height estimates from TDX InSAR data. Our study sites span three major forest types: a temperate forest, a mountainous conifer forest, and a tropical rainforest. The GEDI lidar coverage was simulated for the full nominal two-year mission duration, under both cloud-free and 50%-cloud conditions. We then used these GEDI data to parameterize the Random Volume over Ground (RVoG) model driven by TDX imagery. In particular, we explored the following three strategies for forest structure estimation: 1) TDX data alone; 2) TDX + GEDI-derived digital terrain model (DTM); and 3) TDX + GEDI DTM + GEDI canopy height. We then validated the retrieved forest heights against wall-to-wall airborne lidar measurements. We found relatively large biases at 90 [m] spatial resolution, from 4.2–11.9 [m], and root mean square errors (RMSEs), from 7.9–12.7 [m] when using TDX data alone under constrained RVoG assumptions of a fixed extinction coefficient (σ) and a zero ground-to-volume amplitude ratio (μ = 0). Results improved significantly with the aid of a DTM derived from GEDI data which enabled estimation of spatially-varying σ values (vs. fixed extinction) under a μ = 0 assumption, with biases reduced to 1.7–4.2 [m] and RMSEs to 4.9–8.6 [m] across cloudy and cloud-free cases. The best agreement was achieved in the third strategy by also incorporating information of GEDI-derived canopy height to further enhance the RVoG parameters. The improved model, when still assuming μ = 0, reduced biases to less than or close to 1 m and further reduced RMSEs to 4.0–6.7 [m]. Finally, we used GEDI data to estimate spatially-varying μ in the RVoG model. We found biases of between −0.7–0.9 [m] and RMSEs in the range from 2.6–7.1 [m] over the three sites. Our results suggest that use of GEDI data improves height inversion from TDX, providing heights at more accuracy than can be achieved by TDX alone, and enabling wall-to-wall height estimation at much finer spatial resolution than can be achieved by GEDI alone.

Biomass estimation in dense tropical forest using multiple information from single-baseline P-band PolInSAR data

With the upcoming BIOMASS mission, P-band PolInSAR is expected to provide new perspectives on global forest aboveground biomass (AGB). However, its performance has not yet been fully evaluated for dense tropical forests with complex structure and very high biomass. Based on the TropiSAR campaign in French Guiana, we explored the challenges of the three most commonly used PolInSAR measures to capture AGB in tropical forests; coherence magnitude, interferometric phase, and backscatter. An improved AGB estimation approach was developed by integrating multiple information derived from single-baseline PolInSAR data. The approach involves ground-volume backscatter decomposition and combines volume backscatter with the retrieved forest height. Volume backscatter from the forest canopy was the best predictor of AGB for tropical forests, whereas the ground backscatter contribution was affected by the complex underlying surface and terrain slope. Both LiDAR- and PolInSAR-derived forest heights showed limited correlation with high AGB due to the varying forest basal area. The linear combination of PolInSAR-derived forest height and volume backscatter complemented each other and produced improved AGB estimates. Comparing three different PolInSAR data pairs, the proposed method produced an AGB map with an average R2 of 0.7 and RMSE of 34 tons/ha (relative RMSE of 9.4%) at a spatial resolution of 125 × 125 m2 for biomass between 250–500 tons/ha.

Forest classification and impact of BIOMASS resolution on forest area and aboveground biomass estimation

The European Space Agency (ESA) is currently implementing the BIOMASS mission as 7th Earth Explorer satellite. BIOMASS will provide for the first time global forest aboveground biomass estimates based on P-band synthetic aperture radar (SAR) imagery. This paper addresses an often overlooked element of the data processing chain required to ensure reliable and accurate forest biomass estimates: accurate identification of forest areas ahead of the inversion of radar data into forest biomass estimates.The use of the P-band data from BIOMASS itself for the classification into forest and non-forest land cover types is assessed in this paper. For airborne data in tropical, hemi-boreal and boreal forests we demonstrate that classification accuracies from 90 up to 97% can be achieved using radar backscatter and phase information. However, spaceborne data will have a lower resolution and higher noise level compared to airborne data and a higher probability of mixed pixels containing multiple land cover types. Therefore, airborne data was reduced to 50m, 100m and 200m resolution. The analysis revealed that about 50–60% of the area within the resolution level must be covered by forest to classify a pixel with higher probability as forest compared to non-forest. This results in forest omission and commission leading to similar forest area estimation over all resolutions. However, the forest omission resulted in a biased underestimated biomass, which was not equaled by the forest commission. The results underline the necessity of a highly accurate pre-classification of SAR data for an accurate unbiased aboveground biomass estimation.

Nationally Representative Plot Network Reveals Contrasting Drivers of Net Biomass Change in Secondary and Old-Growth Forests

Uncertainty about the mechanisms driving biomass change at broad spatial scales limits our ability to predict the response of forest biomass storage to global change. Here we use a spatially representative network of 874 forest plots in New Zealand to examine whether commonly hypothesised drivers of forest biomass and biomass change (diversity, disturbance, nutrients and climate) differ between old-growth and secondary forests at a national scale. We calculate biomass stocks and net biomass change for live above-ground biomass, below-ground biomass, deadwood and litter pools. We combine these data with plot-level information on forest type, tree diversity, plant functional traits, climate and disturbance history, and use structural equation models to identify the major drivers of biomass change. Over the period 2002–2014, secondary forest biomass increased by 2.78 (1.68–3.89) Mg ha−1 y−1, whereas no significant change was detected in old-growth forests (+0.28; −0.72 to 1.29 Mg ha−1 y−1). The drivers of biomass and biomass change differed between secondary and old-growth forests. Plot-level biomass change of old-growth forest was driven by recent disturbance (large tree mortality within the last decade), whereas biomass change of secondary forest was determined by current biomass and past anthropogenic disturbance. Climate indirectly affected biomass change through its relationship with past anthropogenic disturbance. Our results highlight the importance of disturbance and disturbance history in determining broad-scale patterns of forest biomass change and suggest that explicitly modelling processes driving biomass change within secondary and old-growth forests is essential for predicting future changes in global forest biomass.

Relasphone—Mobile and Participative In Situ Forest Biomass Measurements Supporting Satellite Image Mapping

Due to the high cost of traditional forest plot measurements, the availability of up-to-date in situ forest inventory data has been a bottleneck for remote sensing image analysis in support of the important global forest biomass mapping. Capitalizing on the proliferation of smartphones, citizen science is a promising approach to increase spatial and temporal coverages of in situ forest observations in a cost-effective way. Digital cameras can be used as a relascope device to measure basal area, a forest density variable that is closely related to biomass. In this paper, we present the Relasphone mobile application with extensive accuracy assessment in two mixed forest sites from different biomes. Basal area measurements in Finland (boreal zone) were in good agreement with reference forest inventory plot data on pine ( R 2 = 0 . 75 , R M S E = 5 . 33 m 2 /ha), spruce ( R 2 = 0 . 75 , R M S E = 6 . 73 m 2 /ha) and birch ( R 2 = 0 . 71 , R M S E = 4 . 98 m 2 /ha), with total relative R M S E ( % ) = 29 . 66 % . In Durango, Mexico (temperate zone), Relasphone stem volume measurements were best for pine ( R 2 = 0 . 88 , R M S E = 32 . 46 m 3 /ha) and total stem volume ( R 2 = 0 . 87 , R M S E = 35 . 21 m 3 /ha). Relasphone data were then successfully utilized as the only reference data in combination with optical satellite images to produce biomass maps. The Relasphone concept has been validated for future use by citizens in other locations.