Aboveground Biomass Values Research Articles

Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation

The data saturation problem in Landsat imagery is well recognized and is regarded as an important factor resulting in inaccurate forest aboveground biomass (AGB) estimation. However, no study has examined the saturation values for different vegetation types such as coniferous and broadleaf forests. The objective of this study is to estimate the saturation values in Landsat imagery for different vegetation types in a subtropical region and to explore approaches to improving forest AGB estimation. Landsat Thematic Mapper imagery, digital elevation model data, and field measurements in Zhejiang province of Eastern China were used. Correlation analysis and scatterplots were first used to examine specific spectral bands and their relationships with AGB. A spherical model was then used to quantitatively estimate the saturation value of AGB for each vegetation type. A stratification of vegetation types and/or slope aspects was used to determine the potential to improve AGB estimation performance by developing a specific AGB estimation model for each category. Stepwise regression analysis based on Landsat spectral signatures and textures using grey-level co-occurrence matrix (GLCM) was used to develop AGB estimation models for different scenarios: non-stratification, stratification based on either vegetation types, slope aspects, or the combination of vegetation types and slope aspects. The results indicate that pine forest and mixed forest have the highest AGB saturation values (159 and 152 Mg/ha, respectively), Chinese fir and broadleaf forest have lower saturation values (143 and 123 Mg/ha, respectively), and bamboo forest and shrub have the lowest saturation values (75 and 55 Mg/ha, respectively). The stratification based on either vegetation types or slope aspects provided smaller root mean squared errors (RMSEs) than non-stratification. The AGB estimation models based on stratification of both vegetation types and slope aspects provided the most accurate estimation with the smallest RMSE of 24.5 Mg/ha. Relatively low AGB (e.g., less than 40 Mg/ha) sites resulted in overestimation and higher AGB (e.g., greater than 140 Mg/ha) sites resulted in underestimation. The smallest RMSE was obtained when AGB was 80–120 Mg/ha. This research indicates the importance of stratification in mitigating the data saturation problem, thus improving AGB estimation.

Above- and Belowground Biomass Allocation in Shrub Biomes across the Northeast Tibetan Plateau.

Biomass partitioning has been explored across various biomes. However, the strategies of allocation in plants still remain contentious. This study investigated allocation patterns of above- and belowground biomass at the community level, using biomass survey from the Tibetan Plateau. We explored above- and belowground biomass by conducting three consecutive sampling campaigns across shrub biomes on the northeast Tibetan Plateau during 2011–2013. We then documented the above-ground biomass (AGB), below-ground biomass (BGB) and root: shoot ratio (R/S) and the relationships between R/S and environment factors using data from 201 plots surveyed from 67 sites. We further examined relationships between above-ground and below-ground biomass across various shrub types. Our results indicated that the median values of AGB, BGB, and R/S in Tibetan shrub were 1102.55, 874.91 g m-2, and 0.85, respectively. R/S showed significant trend with mean annual precipitation (MAP), while decreased with mean annual temperature (MAT). Reduced major axis analysis indicated that the slope of the log-log relationship between above- and belowground biomass revealed a significant difference from 1.0 over space, supporting the optimal hypothesis. Interestingly, the slopes of the allometric relationship between log AGB and log BGB differed significantly between alpine and desert shrub. Our findings supported the optimal theory of above- and belowground biomass partitioning in Tibetan shrub, while the isometric hypothesis for alpine shrub at the community level.

Pollutant removal efficiency of a pilot-scale Horizontal Subsurface Flow in Sicily (Italy) planted with Cyperus alternifolius L. and Typha latifolia L. and reuse of treated wastewater for irrigation of Arundo donax L. for pellet production—Results of two-year tests under Mediterranean climatic conditions

Constructed wetland systems (CWs) are developed biological technologies for the treatment and reuse of wastewater. The aims of this study were to evaluate the treatment performance of a pilot Horizontal Subsurface Flow system (HSSFs), to evaluate the reuse of treated wastewater (TWW) from CWs for the irrigation of giant reed (Arundo donax (L.)) and to assess the effects of TWW on the biomass yield of giant reed grown for pellet production. The research was carried out in Sicily (Italy) in a pilot-scale HSSFs which was fed with urban-treated urban wastewater following secondary treatment from an activated sludge wastewater treatment plant. Giant reed plots were irrigated with TWW from plantedunits, TWW from an unplanted-unit and with freshwater (FW). The pilot system was found to have excellent removal efficiency for all the parameters examined. The different treatment levels of irrigation did not affect significantly the growth and yield of giant reed ecotypes. Differences between heating values of above-ground biomass irrigated with FW and TWW were negligible. TWW from CWs can be used for the irrigation of species grown for energy purposes, thereby providing an alternative source of water, particularly in areas where water deficit in the agricultural sector is significant.

Aboveground Biomass and Carbon in a South African Mistbelt Forest and the Relationships with Tree Species Diversity and Forest Structures

Biomass and carbon stocks are key information criteria to understand the role of forests in regulating global climate. However, for a bio-rich continent like Africa, ground-based measurements for accurate estimation of carbon are scarce, and the variables affecting the forest carbon are not well understood. Here, we present the first biomass study conducted in South Africa Mistbelt forests. Using data from a non-destructive sampling of 59 trees of four species, we (1) evaluated the accuracy of multispecies aboveground biomass (AGB) models, using predictors such as diameter at breast height (DBH), total height (H) and wood density; (2) estimated the amount of biomass and carbon stored in the aboveground compartment of Mistbelt forests and (3) explored the variation of aboveground carbon (AGC) in relation to tree species diversity and structural variables. We found significant effects of species on wood density and AGB. Among the candidate models, the model that incorporated DBH and H as a compound variable (DBH2 × H) was the best fitting. AGB and AGC values were highly variable across all plots, with average values of 358.1 Mg·ha−1 and 179.0 Mg·C·ha−1, respectively. Few species contributed 80% of AGC stock, probably as a result of selection effect. Stand basal area, basal area of the ten most important species and basal area of the largest trees were the most influencing variables. Tree species richness was also positively correlated with AGC, but the basal area of smaller trees was not. These results enable insights into the role of biodiversity in maintaining carbon storage and the possibilities for sustainable strategies for timber harvesting without risk of significant biomass decline.

Quantifying uncertainty in crop model predictions due to the uncertainty in the observations used for calibration

Despite modellers are paying increasing attention to analyse and manage the different sources of uncertainty affecting model predictions, the impact of the uncertainty in the observations used for calibration has been ignored. This study proposes a methodology for its quantification and provides an illustrative case study with data collected in two field experiments where rice was grown under flooded conditions in northern Italy in 2002 and 2004. Latin hypercube sampling was used to generate virtual series of observations from the mean and standard deviation of aboveground biomass values collected during the season in the two experiments. Each of the generated series was then used to calibrate the parameters maximum radiation use efficiency and optimum temperature for growth of the WARM model by means of the simplex optimization algorithm. The analysis of the distribution of key outputs (aboveground and panicle biomass at harvest) and of agreement metrics revealed that the impact of uncertainty in the observations used for calibration (explored here running calibration experiments for each of the generated series) can be large. The difference between maximum and minimum aboveground biomass at maturity was 2.79tha−1 and 3.78tha−1 for the datasets collected in 2004 and 2002, respectively. Corresponding values for panicle biomass were 0.97tha−1 and 2.36tha−1. In all cases, model outputs were normally distributed. Large differences were achieved also in the values of the agreement metrics, with RRMSE ranging from 13.64% to 36.22% and from 8.04% to 29.97% for the 2004 and 2002 datasets. The methodology proposed – although applicable to a variety of models and domains – deals only with the uncertainty due to random errors, which could derive, e.g. from non-representative sampling or from the repeatability of the method used to determine the variable of interest. Other sources of uncertainty, like those involved with systematic errors, need to be addressed in further studies. This study highlighted the need for conceptual and mathematical frameworks where the different sources of uncertainty affecting model predictions can be analysed in an integrated way.

Aboveground biomass and concentration of nutrients in semiarid rangeland plant species: Influence of grazing and soil moisture

Spatial and temporal patterns of aboveground biomass and nutritive value of rangeland species with respect to the influence of grazing and soil moisture were investigated. The research was conducted during two years at the To... | Find, read and cite all the research you need on Tech Science Press

Non-Parametric Retrieval of Aboveground Biomass in Siberian Boreal Forests with ALOS PALSAR Interferometric Coherence and Backscatter Intensity

The main objective of this paper is to investigate the effectiveness of two recently popular non-parametric models for aboveground biomass (AGB) retrieval from Synthetic Aperture Radar (SAR) L-band backscatter intensity and coherence images. An area in Siberian boreal forests was selected for this study. The results demonstrated that relatively high estimation accuracy can be obtained at a spatial resolution of 50 m using the MaxEnt and the Random Forests machine learning algorithms. Overall, the AGB estimation errors were similar for both tested models (approximately 35 t∙ha−1). The retrieval accuracy slightly increased, by approximately 1%, when the filtered backscatter intensity was used. Random Forests underestimated the AGB values, whereas MaxEnt overestimated the AGB values.

Scaling mangrove aboveground biomass from site‐level to continental‐scale

AbstractAimWe developed a set of statistical models to improve spatial estimates of mangrove aboveground biomass (AGB) based on the environmental signature hypothesis (ESH). We hypothesized that higher tidal amplitudes, river discharge, temperature, direct rainfall and decreased potential evapotranspiration explain observed high mangrove AGB.LocationNeotropics and a small portion of the Nearctic region.MethodsA universal forest model based on site‐level forest structure statistics was validated to spatially interpolate estimates of mangrove biomass at different locations. Linear models were then used to predict mangrove AGB across the Neotropics.ResultsThe universal forest site‐level model was effective in estimating mangrove AGB using pre‐existing mangrove forest structure inventories to validate the model. We confirmed our hypothesis that at continental scales higher tidal amplitudes contributed to high forest biomass associated with high temperature and rainfall, and low potential evapotranspiration. Our model explained 20% of the spatial variability in mangrove AGB, with values ranging from 16.6 to 627.0 t ha−1 (mean, 88.7 t ha−1). Our findings show that mangrove AGB has been overestimated by 25–50% in the Neotropics, underscoring a commensurate bias in current published global estimates using site‐level information.Main conclusionsOur analysis show how the ESH significantly explains spatial variability in mangrove AGB at hemispheric scales. This finding is critical to improve and explain site‐level estimates of mangrove AGB that are currently used to determine the relative contribution of mangrove wetlands to global carbon budgets. Due to the lack of a conceptual framework explicitly linking environmental drivers and mangrove AGB values during model validation, previous works have significantly overestimated mangrove AGB; our novel approach improved these assessments. In addition, our framework can potentially be applied to other forest‐dominated ecosystems by allowing the retrieval of extensive databases at local levels to generate more robust statistical predictive models to estimate continental‐scale biomass values.

Changes in aboveground biomass following alternative harvesting in oak-hickory forests in the eastern USA

Abstract: Managing forest lands for the sustainability of ecosystem functions and services by developing and implementing sound silvicultural methods through site-specific practices is a core concept in ecosystem management. In this study, we used long-term data collected at the extensive plots of the Missouri Forest Ecosystem Project (MOFEP) in the southeastern Missouri Ozarks (USA) to study the changes in aboveground biomass (AGB) under three silvicultural treatments: even-aged management sites (EAM), uneven-aged management sites (UAM), and non-harvested management sites (NHM). Treatments changed the magnitude of AGB dynamics. The forests maintained an AGB of 147.9 Mg ha-1 in 1990 and it increased to 175.6 Mg ha-1 by 2009. The forests were manipulated with four treatments: clear-cut, non-harvest, uneven-aged single-tree, and uneven-aged group selection and yielded AGB values of 30.7, 139.5, 125.7, and 148.7 Mg ha-1 of AGB in 2009, respectively. Over the 18-year study period, these forests accumulated 1.78 ± 0.26 Mg ha-1 yr-1, ranging from 1.60 to 1.94 Mg ha-1 yr-1 at the NHM plots. Changes in the net AGB growth rate were contributed by different growth rates of live trees and mortality and exhibited clear intra-annual variation during the five sampling periods. We observed a decreasing contribution of Quercus velutina (black oak) AGB (~6%), an increasing trend for Q. alba (white oak), and a stable change for Q. coccinea (scarlet oak) during the study period.

Assessment of Above-Ground Biomass of Borneo Forests through a New Data-Fusion Approach Combining Two Pan-Tropical Biomass Maps

This study investigates how two existing pan-tropical above-ground biomass (AGB) maps (Saatchi 2011, Baccini 2012) can be combined to derive forest ecosystem specific carbon estimates. Several data-fusion models which combine these AGB maps according to their local correlations with independent datasets such as the spectral bands of SPOT VEGETATION imagery are analyzed. Indeed these spectral bands convey information about vegetation type and structure which can be related to biomass values. Our study area is the island of Borneo. The data-fusion models are evaluated against a reference AGB map available for two forest concessions in Sabah. The highest accuracy was achieved by a model which combines the AGB maps according to the mean of the local correlation coefficients calculated over different kernel sizes. Combining the resulting AGB map with a new Borneo land cover map (whose overall accuracy has been estimated at 86.5%) leads to average AGB estimates of 279.8 t/ha and 233.1 t/ha for forests and degraded forests respectively. Lowland dipterocarp and mangrove forests have the highest and lowest AGB values (305.8 t/ha and 136.5 t/ha respectively). The AGB of all natural forests amounts to 10.8 Gt mainly stemming from lowland dipterocarp (66.4%), upper dipterocarp (10.9%) and peat swamp forests (10.2%). Degraded forests account for another 2.1 Gt of AGB. One main advantage of our approach is that, once the best fitting data-fusion model is selected, no further AGB reference dataset is required for implementing the data-fusion process. Furthermore, the local harmonization of AGB datasets leads to more spatially precise maps. This approach can easily be extended to other areas in Southeast Asia which are dominated by lowland dipterocarp forest, and can be repeated when newer or more accurate AGB maps become available.

Estimation of Above Ground Biomass for Central Indian Deciduous Forests Using ALOS PALSAR L-Band Data

Reliable and accurate estimates of tropical forest above ground biomass (AGB) are important to reduce uncertainties in carbon budgeting. In the present study we estimated AGB of central Indian deciduous forests of Madhya Pradesh (M.P.) state, India, using Advanced Land Observing Satellite – Phased Array type L-band Synthetic Aperture Radar (ALOS-PALSAR) L-band data of year 2010 in conjunction with field based AGB estimates using empirical models. Digital numbers of gridded 1 × 1° dual polarization (HH & HV) PALSAR mosaics for the study area were converted to normalized radar cross section (sigma naught - σ0). A total of 415 sampling plots (0.1 ha) data collected over the study area during 2009–10 was used in the present study. Plot-level AGB estimates using volume equations representative to the study area were computed using field inventory data. The plot-level AGB estimates were empirically modeled with the PALSAR backscatter information in HH, HV and their ratios from different forest types of the study area. The HV backscatter information showed better relation with field based AGB estimates with a coefficient of determination (R2) of 0.509 which was used to estimate spatial AGB of the study area. Results suggested a total AGB of 367.4 Mt for forests of M.P. state. Further, validation of the model was carried out using observed vs. predicted AGB estimates, which suggested a root mean square error (RMSE) of ±19.32 t/ha. The model reported robust and defensible relation for observed vs. predicted AGB values of the study area.

Foliar interception of radionuclides in dry conditions: a meta-analysis using a Bayesian modeling approach

Uncertainty on the parameters that describe the transfer of radioactive materials into the (terrestrial) environment may be characterized thanks to datasets such as those compiled within International Atomic Energy Agency (IAEA) documents. Nevertheless, the information included in these documents is too poor to derive a relevant and informative uncertainty distribution regarding dry interception of radionuclides by the pasture grass and the leaves of vegetables. In this paper, 145 sets of dry interception measurements by the aboveground biomass of specific plants were collected from published scientific papers. A Bayesian meta-analysis was performed to derive the posterior probability distributions of the parameters that reflect their uncertainty given the collected data. Four competing models were compared in terms of both fitting performances and predictive abilities to reproduce plausible dry interception data. The asymptotic interception factor, applicable whatever the species and radionuclide to the highest aboveground biomass values (e.g. mature leafy vegetables), was estimated with the best model, to be 0.87 with a 95% credible interval (0.85, 0.89).

Geostatistical modeling using LiDAR-derived prior knowledge with SPOT-6 data to estimate temperate forest canopy cover and above-ground biomass via stratified random sampling

Forest canopy cover (CC) and above-ground biomass (AGB) are important ecological indicators for forest monitoring and geoscience applications. This study aimed to estimate temperate forest CC and AGB by integrating airborne LiDAR data with wall-to-wall space-borne SPOT-6 data through geostatistical modeling. Our study involved the following approach: (1) reference maps of CC and AGB were derived from wall-to-wall LiDAR data and calibrated by field measurements; (2) twelve discrete LiDAR flights were simulated by assuming that LiDAR data were only available beneath these flights; (3) training/testing samples of CC and AGB were extracted from the reference maps inside and outside the simulated flights using stratified random sampling; (4) The simple linear regression, ordinary kriging and regression kriging model were used to extend the sparsely sampled CC/AGB data to the entire study area by incorporating a selection of SPOT-6 variables, including vegetation indices and texture variables. The regression kriging model was superior at estimating and mapping the spatial distribution of CC and AGB, as it featured the lowest mean absolute error (MAE; 11.295% and 18.929 t/ha for CC and AGB, respectively) and root mean squared error (RMSE; 17.361% and 21.351 t/ha for CC and AGB, respectively). The predicted and reference values of both CC and AGB were highly correlated for the entire study area based on the estimation histograms and error maps. Finally, we concluded that the regression kriging model was superior and more effective at estimating LiDAR-derived CC and AGB values using the spatially-reduced samples and the SPOT-6 variables. The presented modeling workflow will greatly facilitate future forest growth monitoring and carbon stock assessments for large areas of temperate forest in northeast China. It also provides guidance on how to take full advantage of future sparsely collected LiDAR data in cases where wall-to-wall LiDAR coverage is not available from the perspective of geostatistics.

L-Band SAR Backscatter Related to Forest Cover, Height and Aboveground Biomass at Multiple Spatial Scales across Denmark

Mapping forest aboveground biomass (AGB) using satellite data is an important task, particularly for reporting of carbon stocks and changes under climate change legislation. It is known that AGB can be mapped using synthetic aperture radar (SAR), but relationships between AGB and radar backscatter may be confounded by variations in biophysical forest structure (density, height or cover fraction) and differences in the resolution of satellite and ground data. Here, we attempt to quantify the effect of these factors by relating L-band ALOS PALSAR HV backscatter and unique country-wide LiDAR-derived maps of vegetation penetrability, height and AGB over Denmark at different spatial scales (50 m to 500 m). Trends in the relations indicate that, first, AGB retrieval accuracy from SAR improves most in mapping at 100-m scale instead of 50 m, and improvements are negligible beyond 250 m. Relative errors (bias and root mean squared error) decrease particularly for high AGB values (\(>\)110 Mg ha\(^{-1}\)) at coarse scales, and hence, coarse-scale mapping (\(\ge\)150 m) may be most suited for areas with high AGB. Second, SAR backscatter and a LiDAR-derived measure of fractional forest cover were found to have a strong linear relation (R\(^2\) = 0.79 at 250-m scale). In areas of high fractional forest cover, there is a slight decline in backscatter as AGB increases, indicating signal attenuation. The two results demonstrate that accounting for spatial scale and variations in forest structure, such as cover fraction, will greatly benefit establishing adequate plot-sizes for SAR calibration and the accuracy of derived AGB maps.

Seasonal dynamics of yellow water lily Nuphur lutea (L.) Smith (Nymphaeaceae) in the small Ild River (Yaroslavl oblast)

Several peaks have been determined in the seasonal dynamics of the aboveground biomass of the yellow water lily. The aboveground biomass reaches its maximal values during the period of mass floweringearly fruiting. The accumulation of plant litter during the growing season occurs unevenly; its maximal values are reached when one type of leaves is replaced by the other. The net aboveground annual production of yellow water lily exceeds on average two times the maximum values of aboveground biomass. It is estimated that 1 m2 of overgrowths yields on average 349 g of organic carbon.

Empirical models to predict LAI and aboveground biomass of Coffea arabica under full sun and shaded plantation: a case study of South of Minas Gerais, Brazil

Leaf area index (LAI) and above ground biomass (AGB) are two parameters that are difficult to measure but very useful. In this paper we investigated the relationship between coffee biophysical properties and LAI and AGB in two coffee production systems: full sun (FS) and shaded with macadamia nuts (SH). The paper proposes an empirical relationship for calculating coffee AGB and coffee LAI which avoids destructive methods, using simple field measurements and agrometeorological data. Here, we reported that LAI is related to canopy structure but subject to strong seasonal variations, which can be identified using water requirements satisfaction index (WRSI). Coffee LAI answers to the decreased WRSI with 1 month lag (WRSI-1) and LAI values decreases more for FS systems than for SH systems during dry periods. The best empirical model to predict LAI for FS coffee production system was based on canopy height (ch) and WRSI-1 value. For SH systems, the best model used ch, WRSI-1 and the height of the first pair of branches. Coffee AGB values were measured using destructive analyses and an empirical equation was developed. Both coffee production systems stocked carbon, whereas the SH system stocked an increased carbon amount provided by the macadamia trees that contributed with 15 % of the total carbon above ground. Both systems can be considered mitigation techniques since they are able to remove atmospheric carbon and stock it in the biomass, which has been largely proposed as a compensation mechanism for greenhouse gas emissions.

Contribución de los bosques tropicales de montaña en el almacenamiento de carbono en Colombia

The tropical montane forests in the Colombian Andean region are located above 1500 m, and have been heavily deforested. Despite the general presumption that productivity and hence carbon stocks in these ecosystems are low, studies in this regard are scarce. This study aimed to (i) to estimate Above Ground Biomass (AGB) in forests located in the South of the Colombian Andean region, (ii) to identify the carbon storage potential of tropical montane forests dominated by the black oak Colombobalanus excelsa and to identify the relationship between AGB and altitude, and (iii) to analyze the role of tropical mountain forests in conservation mechanisms such as Payment for Environmental Services (PES) and Reducing Emissions from Deforestation and Degradation (REDD+). Twenty six 0.25 ha plots were randomly distributed in the forests and all trees with D > or =10 cm were measured. The results provided important elements for understanding the role of tropical montane forests as carbon sinks. The information produced can be used in subnational initiatives, which seek to mitigate or reduce the effects of deforestation through management or conservation of these ecosystems, like REDD+ or PES. The AGB and carbon stocks results obtained were similar to those reported for lowland tropical forests. These could be explained by the dominance and abundance of C. excelsa, which accounted for over 81% of AGB/carbon. The error associated with the estimates of AGB/carbon was 10.58%. We found a negative and significant relationship between AGB and altitude, but the higher AGB values were in middle altitudes (approximatly = 700-1800 m), where the environmental conditions could be favorable to their growth. The carbon storage potential of these forests was higher. However, if the historical rate of the deforestation in the study area continues, the gross emissions of CO2e to the atmosphere could turn these forests in to an important emissions source. Nowadays, it is clear that tropical montane forests are vulnerable to deforestation, especially black Oak forests due to their commercial value. Given their high carbon storage potential, the presence of endemic species and the strategic functions of these ecosystems, we recommend that they should be considered relevant during REDD+, PES or any other conservation assessment.

The evaluation of different forest structural indices to predict the stand aboveground biomass of even-aged Scotch pine (Pinus sylvestris L.) forests in Kunduz, Northern Turkey

We assessed the effect of stand structural diversity, including the Shannon, improved Shannon, Simpson, McIntosh, Margelef, and Berger-Parker indices, on stand aboveground biomass (AGB) and developed statistical prediction models for the stand AGB values, including stand structural diversity indices and some stand attributes. The AGB prediction model, including only stand attributes, accounted for 85% of the total variance in AGB (R (2)) with an Akaike's information criterion (AIC) of 807.2407, Bayesian information criterion (BIC) of 809.5397, Schwarz Bayesian criterion (SBC) of 818.0426, and root mean square error (RMSE) of 38.529Mg. After inclusion of the stand structural diversity into the model structure, considerable improvement was observed in statistical accuracy, including 97.5% of the total variance in AGB, with an AIC of 614.1819, BIC of 617.1242, SBC of 633.0853, and RMSE of 15.8153Mg. The predictive fitting results indicate that some indices describing the stand structural diversity can be employed as significant independent variables to predict the AGB production of the Scotch pine stand. Further, including the stand diversity indices in the AGB prediction model with the stand attributes provided important predictive contributions in estimating the total variance in AGB.

Capabilities of BIOMASS Tomography for Investigating Tropical Forests

The objective of this paper is to provide a better understanding of the capabilities of the BIOMASS tomography concerning the retrieval of forest biomass and height in tropical areas. The analysis presented in this paper is carried out on airborne data acquired by Office National d'Etudes et de Recherches Aérospatiales (ONERA) over the site of Paracou, French Guiana, during the European Space Agency campaign TropiSAR. This high-resolution data set (125-MHz bandwidth) was reprocessed in order to generate a new data stack consistent with BIOMASS as for the bandwidth (6 MHz) and the azimuth resolution (about 12 m). To do this, two different processing approaches have been considered. One approach consisted of degrading the resolution of the airborne data through the linear filtering of raw data, followed by standard SAR processing. The other approach consisted of recovering the 3-D distribution of the scatterers at a high resolution, which was then reprojected onto the BIOMASS geometry. The latter procedure allows us to obtain a data stack that is the most realistic emulation of BIOMASS imaging capabilities. In both approaches, neither ionospheric disturbances nor temporal decorrelation has been considered. The connection to the forest biomass has been examined in both cases by investigating the correlation between the backscatter at different forest heights and the above-ground biomass (AGB) values from in situ data. As expected, the reduction of the system bandwidth to 6 MHz resulted in significant vertical resolution losses compared with the original airborne data. Nevertheless, it was possible to retrieve the forest height to within an accuracy of better than 4 m, whereas the backscattered power at the volume height (30 m above the ground) exhibited a correlation higher than 0.8 with the in situ data and no bias phenomena over the AGB values ranging from 250 to 450 t/ha.

Estimating the Aboveground Dry Biomass of Grass by Assimilation of Retrieved LAI Into a Crop Growth Model

This study presents a method to assimilate leaf area index (LAI) retrieved from MODIS data using a physically based method into a soil-water-atmosphere-plant (SWAP) model to estimate the aboveground dry biomass of grass in the Ruoergai grassland, China. The assimilation method consists of reinitializing the model with optimal input parameters that allow a better temporal agreement between the LAI simulated by the SWAP model and the LA! retrieved from MODIS data. The minimization is performed by a four-dimensional variational data assimilation (4D-VAR) algorithm but which is challenged by the development of the adjoint model. The automatic differentiation (AD) technique is thus used to provide the adjoint model at the level of computer language codes. After the re-initialization, the simulated aboveground dry biomass value is compared with ground measurements taken in early August2013. The results show that the biomass can be estimated with highly satisfactory accuracy level through the assimilation method with R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (the deterministic coefficient) = 0.73 and RMSE(root-mean-square error) = 617.94 kg ha <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> . The accuracy is further improved when the newly derived RMSELAI values are used as observation errors in the assimilation process, with R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.76 and RMSE = 542.52 kgha <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> . Both assimilation strategies yield a significant improvement in SWAP model accuracy with respect to no significant correlation obtained when the SWAP model is run alone with constant values of the input parameters employed for the whole area. The validity of the 4D-VAR method for biomass estimation is well demonstrated.