Uncertainty of Forest Biomass Estimates in North Temperate Forests Due to Allometry: Implications for Remote Sensing

Estimates of above ground biomass density in forests are crucial for refining global climate models and understanding climate change. Although data from field studies can be aggregated to estimate carbon stocks on global scales, the sparsity of such field data, temporal heterogeneity and methodological variations introduce large errors. Remote sensing measurements from spaceborne sensors are a realistic alternative for global carbon accounting; however, the uncertainty of such measurements is not well known and remains an active area of research. This article describes an effort to collect field data at the Harvard and Howland Forest sites, set in the temperate forests of the Northeastern United States in an attempt to establish ground truth forest biomass for calibration of remote sensing measurements. We present an assessment of the quality of ground truth biomass estimates derived from three different sets of diameter-based allometric equations over the Harvard and Howland Forests to establish the contribution of errors in ground truth data to the error in biomass estimates from remote sensing measurements

Spectral radiometric technique for carbon estimation in Omo Forest Reserve, South Western, Nigeria

Field-estimated above-ground biomass (AGB) and spectral data from remote sensing were collected from randomly selected 50 sample plots. AGB  was estimated through the biomass density equation. Radiometric measurements were carried out using a set of spectral vegetation indices. The  remote sensing data was calibrated with those obtained from the field using GPS points. The average model-based estimation using satellite image canopy cover was 30.71 t/plot, while the multispectral data was 69.07 t/plot in the biosphere. This gave a difference of 1.44 t/plot and 36.91 t/plot  respectively from the calculated carbon 32.16 t/plot. The canopy cover based estimation deviated from the ground measurement with 1.44 t/plot, while the estimation based on vegetation indices was twice that of field measurement. The result indicated that calibrated field measurements with forest canopy cover from high resolution image was the most reliable remote sensing technique in estimating AGB in a natural forest as compared  to vegetation index. The model selected for a single tree forest based on modified soil adjusted vegetation index with value of 61.18 t/plot compared to the calculated value of 49.84 t/plot may to some extent improve AGB estimation. Keywords: Carbon sink, Biosphere, Above-ground biomass, Vegetation index and Remote sensin

APPLICATION OF REMOTE SENSING TO ESTIMATE ABOVE GROUND BIOMASS IN TROPICAL FORESTS OF INDONESIA

This work aims to estimate Above Ground biomass (AGB) of a tropical rainforest in East Kalimantan, Indonesia using equation derived from the stand volume prediction and to study the spatial distribution of AGB over aforest area. The potential of remote sensing and field measurement data to predict stand volume and AGB were studied Landsat ElM data were atmospherically corrected using Dark Object Subtraction (DOS) technique, and topographic corrections were conducted using C-correction method Stand volume was estimated using field data and remote sensing data using Levenberg-Marquardt neural networks. Stand volume data was converted into the above ground biomass using available volume - AGB equations. Spatial distribution of the AGB and the error estimate were then interpolated using kriging. Validated with observation data, the stand volume estimate showed integration of field measurement and remote sensing data has better prediction than the solitary uses of those data. The AGB estimate showed good correlations with stand volume, number of stems, and basal area

Estimating Above Ground Biomass using Remote Sensing in the Sub-Tropical Climate Zones of Australia

The study focused on assessing the total above ground biomass using remote sensing in the Kimberley rangelands of Western Australia. Remote sensing has the advantage that it can rapidly provide estimates non-destructively on a large scale with a high temporal frequency. In this thesis a field sampling protocol was developed and mono- and multi-temporal above ground biomass estimation models could be calibrated and validated with field based measurements for the most significant vegetation types

Mapping of urban above-ground biomass with high resolution remote sensing data

This paper reports on study carried out to determine and map the distributions and density of the urban total above-ground biomass (TAGB) content using high resolution satellite data of the SPOT-4 and Quickbird, with respective 10 and 4 meter spatial resolution for mapping two levels of urban biomass, Level I biomass derived at selected residential areas in Johor Bahru city urban landscape, and Level II biomass derived from SPOT-4 data in for the entire urban district (including the suburbs). The results of this study indicated that, Level I and Level II of biomass were derived at respective accuracy of ±0.3kgm-2 and ±0.4kgm-2, validated with in-situ verification

Using P-band Signals of Opportunity Radio Waves for Root Zone Soil Moisture Remote Sensing

Retrieval of Root Zone Soil Moisture (RZSM) is important for understanding the carbon cycle for use in climate change research as well as meteorology, hydrology, and precision agriculture studies. A current method of remote sensing, GNSS-R uses GPS signals to measure soil moisture content and vegetation biomass, but it is limited to 3-5 cm of soil penetration depth. Signals of Opportunity (SoOp) has emerged as an extension of GNSS-R remote sensing using communication signals. P-band communication signals (370 MHz) will be studied as an improved method of remote sensing of RZSM. P-band offers numerous advantages over GNSS-R, including stronger signal strength and deeper soil penetration. A SoOp instrument was installed on a mobile antenna tower in a farm field at Purdue University in West Lafayette, IN. An additional half-wave dipole antenna, as well as corresponding modifications to the experiment’s front-end box, was included to capture horizontally-polarized reflected P-band signals throughout a corn growth season. By measuring the reflected signal power off the soil over time, soil moisture and above-ground biomass can be measured. Soil moisture and vegetation biomass change the soil’s dielectric reflection coefficient and thus affect its reflectivity properties. It is expected that there will be strong correlation between reflected signal strength and soil moisture. Data will be compared against soil moisture measurements from in-situ soil sensors. The data obtained will be used to verify existing analytical soil moisture and above-ground biomass models. In addition, these results will be used to build an airborne and/or space-based remote sensing instrument

Biomass estimation as a function of vertical forest structure and forest height. Potential and limiations for remote sensing (radar and LiDAR)

Forest biomass stock, spatial distribution and dynamics are unknown parameters for many regions of the world. Today’s information is largely based on ground measurements on a plot basis without coverage in many remote regions that are fundamental for the global carbon cycle. Thus, a method capable of quantifying biomass by means of Remote Sensing (RS) could help to reduce these uncertainties and contribute to a better understanding of it. In this study the capacity to improve the estimation of above-ground biomass (AGB) with a new approach based on forest vertical structure and its potential to improve RS estimations is analyzed. Height to biomass allometry allows biomass estimations from remote sensing systems capable to resolve forest height (LiDAR and polarimetric SAR interferometry (Pol-InSAR)). However, this approach meets its limitations for forest ecosystems under changing conditions in density and structure. To improve biomass estimation accuracy, additional parameters need to be measured. Pol-InSAR and LiDAR allow getting besides forest height vertical backscattering profiles which are connected to forest vertical structure. Thus, due to the relation between structural parameters and AGB expressed by the Structure to Biomass allometry, AGB can be potentially inverted from these systems. The best characterization of forest vertical structure is obtained using the Legendre polynomials. Biomass profiles can be then characterized by the decomposition into a set of Legendre-Fourier basis functions. This method is able to accurately reconstruct vertical biomass profiles with low frequency features. Vertical backscattering profiles are strongly dependent on the sensor used as the resulting profiles are very sensitive to the wavelength and system geometry. E.g. LiDAR profiles are more sensitive to leaves and crowns while Pol-InSAR tends to reconstruct more the woody compartments (stems and branches). In this study, vertical backscattering profiles from short footprint airborne LiDAR and Pol-InSAR data are evaluated for their potential to reconstruct vertical forest structure. With the Legendre decomposition it is possible to parameterize the vertical backscattering profiles and relate them to forest biomass; even though for each remote sensing system different calibration methodologies must be derived. A first step is achieved using the calibration of backscattering signal with known biomass levels showing optimum results. In order to reduce the need of known parameters a new calibration methodology that exploits height to biomass allometric relations has been derived. Inversions using this methodology are tested for LiDAR and SAR profiles showing good correlations for an optimum subset of samples. As each system (frequency) is sensitive to certain biomass components an underestimation is generally expected. Research in this area is ongoing and will be presented with special focus on each system capacity to reconstruct forest vertical biomass distribution for broader sets of samples

Interannual Variability in Dry Mixed-Grass Prairie Yield: A Comparison of MODIS, SPOT, and Field Measurements

Remote sensing is often used to assess rangeland condition and biophysical parameters across large areas. In particular, the relationship between the Normalized Difference Vegetation Index (NDVI) and above-ground biomass can be used to assess rangeland primary productivity (seasonal carbon gain or above-ground biomass “yield”). We evaluated the NDVI–yield relationship for a southern Alberta prairie rangeland, using seasonal trends in NDVI and biomass during the 2009 and 2010 growing seasons, two years with contrasting rainfall regimes. The study compared harvested biomass and NDVI from field spectrometry to NDVI from three satellite platforms: the Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Système Pour l’Observation de la Terre (SPOT 4 and 5). Correlations between ground spectrometry and harvested biomass were also examined for each growing season. The contrasting precipitation patterns were easily captured with satellite NDVI, field NDVI and green biomass measurements. NDVI provided a proxy measure for green plant biomass, and was linearly related to the log of standing green biomass. NDVI phenology clearly detected the green biomass increase at the beginning of each growing season and the subsequent decrease in green biomass at the end of each growing season due to senescence. NDVI–biomass regressions evolved over each growing season due to end-of-season senescence and carryover of dead biomass to the following year. Consequently, mid-summer measurements yielded the strongest correlation (R2 = 0.97) between NDVI and green biomass, particularly when the data were spatially aggregated to better match the satellite sampling scale. Of the three satellite platforms (MODIS Aqua, MODIS Terra, and SPOT), Terra yielded the best agreement with ground-measured NDVI, and SPOT yielded the weakest relationship. When used properly, NDVI from satellite remote sensing can accurately estimate peak-season productivity and detect interannual variation in standing green biomass, and field spectrometry can provide useful validation for satellite data in a biomass monitoring program in this prairie ecosystem. Together, these methods can be used to identify the effects of year-to-year precipitation variability on above-ground biomass in a dry mixed-grass prairie. These findings have clear applications in monitoring yield and productivity, and could be used to support a rangeland carbon monitoring program

Above ground biomass functions with vegetation indices for multiple use systems of two evergreen oaks

Remote sensing is a promising approach for above ground biomass estimation, as forest parameters can be obtained indirectly. The analysis in space and time is quite straight forward due to the flexibility of the method to determine forest crown parameters with remote sensing. It can be used to evaluate and monitoring for example the development of a forest area in time and the impact of disturbances, such as silvicultural practices or deforestation. The vegetation indices, which condense data in a quantitative numeric manner, have been used to estimate several forest parameters, such as the volume, basal area and above ground biomass. The objective of this study was the development of allometric functions to estimate above ground biomass using vegetation indices as independent variables. The vegetation indices used were the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Simple Ratio (SR) and Soil-Adjusted Vegetation Index (SAVI). QuickBird satellite data, with 0.70 m of spatial resolution, was orthorectified, geometrically and atmospheric corrected, and the digital number were converted to top of atmosphere reflectance (ToA). Forest inventory data and published allometric functions at tree level were used to estimate above ground biomass per plot. Linear functions were fitted for the monospecies and multispecies stands of two evergreen oaks (Quercus suber and Quercus rotundifolia) in multiple use systems, montados. The allometric above ground biomass functions were fitted considering the mean and the median of each vegetation index per grid as independent variable. Species composition as a dummy variable was also considered as an independent variable. The linear functions with better performance are those with mean NDVI or mean SR as independent variable. Noteworthy is that the two better functions for monospecies cork oak stands have median NDVI or median SR as independent variable. When species composition dummy variables are included in the function (with stepwise regression) the best model has median NDVI as independent variable. The vegetation indices with the worse model performance were EVI and SAVI

Characterizing degradation of palm swamp peatlands from space and on the ground: an exploratory study in the Peruvian Amazon

Peru has the fourth largest area of peatlands in the Tropics. Its most representative land cover on peat is a Mauritia flexuosa dominated palm swamp (thereafter called dense PS), which has been under human pressure over decades due to the high demand for the M. flexuosa fruit often collected by cutting down the entire palm. Degradation of these carbon dense forests can substantially affect emissions of greenhouse gases and contribute to climate change. The first objective of this research was to assess the impact of dense PS degradation on forest structure and biomass carbon stocks. The second one was to explore the potential of mapping the distribution of dense PS with different degradation levels using remote sensing data and methods. Biomass stocks were measured in 0.25 ha plots established in areas of dense PS with low (n = 2 plots), medium (n = 2) and high degradation (n = 4). We combined field and remote sensing data from the satellites Landsat TM and ALOS/PALSAR to discriminate between areas typifying dense PS with low, medium and high degradation and terra firme, restinga and mixed PS (not M. flexuosa dominated) forests. For this we used a Random Forest machine learning classification algorithm. Results suggest a shift in forest composition from palm to woody tree dominated forest following degradation. We also found that human intervention in dense PS translates into significant reductions in tree carbon stocks with initial (above and below-ground) biomass stocks (135.4 ± 4.8 Mg C ha−1) decreased by 11 and 17% following medium and high degradation. The remote sensing analysis indicates a high separability between dense PS with low degradation from all other categories. Dense PS with medium and high degradation were highly separable from most categories except for restinga forests and mixed PS. Results also showed that data from both active and passive remote sensing sensors are important for the mapping of dense PS degradation. Overall land cover classification accuracy was high (91%). Results from this pilot analysis are encouraging to further explore the use of remote sensing data and methods for monitoring dense PS degradation at broader scales in the Peruvian Amazon. Providing precise estimates on the spatial extent of dense PS degradation and on biomass and peat derived emissions is required for assessing national emissions from forest degradation in Peru and is essential for supporting initiatives aiming at reducing degradation activities