Variation In Aboveground Biomass Research Articles

Assessing the Predictive Power of Democratic Republic of Congo’s National Spaceborne Biomass Map over Independent Test Samples

Remotely sensed maps of forest carbon stocks have enormous potential for supporting greenhouse gas (GHG) inventory and monitoring in tropical countries. However, most countries have not used maps as the reference data for GHG inventory due to the lack of confidence in the accuracy of maps and of data to perform local validation. Here, we use the first national forest inventory (NFI) data of the Democratic Republic of Congo to perform an independent assessment of the country’s latest national spaceborne carbon stocks map. We compared plot-to-plot variations and areal estimates of forest aboveground biomass (AGB) derived from NFI data and from the map across jurisdictional and ecological domains. Across all plots, map predictions were nearly unbiased and captured c. 60% of the variation in NFI plots AGB. Map performance was not uniform along the AGB gradient, and saturated around c. 290 Mg ha−1, increasingly underestimating forest AGB above this threshold. Splitting NFI plots by land cover types, we found map predictions unbiased in the dominant terra firme Humid forest class, while plot-to-plot variations were poorly captured (R2 of c. 0.33, or c. 0.20 after excluding disturbed plots). In contrast, map predictions underestimated AGB by c. 33% in the small AGB woodland savanna class but captured a much greater share of plot-to-plot AGB variation (R2 of c. 0.41, or 0.58 after excluding disturbed plots). Areal estimates from the map and NFI data depicted a similar trend with a slightly smaller (but statistically indiscernible) mean AGB from the map across the entire study area (i.e., 252.7 vs. 280.6 Mg ha−1), owing to the underestimation of mean AGB in the woodland savanna domain (31.8 vs. 57.3 Mg ha−1), which was broadly consistent with the results obtained at the provincial level. This study provides insights and outlooks for country-wide AGB mapping efforts in the tropics and the computation of emission factors in Democratic Republic of Congo for carbon monitoring initiatives.

Calibrating APSIM for forage sorghum using remote sensing and field data under sub-optimal growth conditions

CONTEXTMechanistic sorghum models have been mostly used to estimate sorghum yield for grain sorghum for a range of genotype, management, and environmental conditions. There is a lack of model testing for crop growth and development responses for forage genotypes and information for phenological parameterization under sub-optimal water and nitrogen stress conditions in forage systems. OBJETIVEThe aims of this study were to (i) use NDVI to parametrize APSIM model to estimate forage sorghum phenology, (ii) calibrate APSIM to simulate green cover, intercepted solar radiation and aboveground biomass, and (iii) quantify the variance of inter-annual aboveground biomass and the effect of water availability on forage sorghum biomass under sub-optimal environment × management combinations. METHODSWe used climate, soil, management records and sorghum crop observations collected from farm and field experiments in Argentina and Australia. NDVI values were gathered from Sentinel-2 and a handheld optical sensor and then related to fAPAR measurements. Phenological stages were derived from fAPAR seasonal dynamics and implemented as input in the APSIM calibration. Finally, we assessed the temporal AGB variability through long-term simulations analysis. RESULTS AND CONCLUSIONSNDVI seasonal dynamics accurately represented the fraction of the absorbed photosynthetically active radiation (R2=0.92) and then, the remote-sensing parametrized APSIM model satisfactorily simulated crop phenology (CCC=0.75-0.92, NRMSE=9-22%). The model was also able to satisfactorily simulate crop growth (CCC=0.89 and NRMSE=24.8% for green cover; CCC=0.81 and NRMSE=34.6% for intercepted solar radiation; CCC=0.91 and NRMSE=37% for aboveground biomass). APSIM simulations during 22 years across 5 contrasting locations showed high inter-annual variability of aboveground biomass (CV=47%), mainly driven by inter-annual variation of soil water availability (CV=20%). Our study demonstrated that (i) remote sensing data was a reliable source for APSIM phenology parametrization, (ii) the model was able to satisfactorily simulate crop growth and development of forage sorghum under sub-optimal conditions across several genotype × environment × management combinations and (iii) water availability was the main driver of aboveground biomass inter-annual variance. SIGNIFICANCEGiven the pressure of the global human population to satisfy an increasing demand for food, our results provide a new path for the combined use of remote sensing and mechanistic modelling to improve forage sorghum biomass estimations in marginal environments.

Homegardens as a modern carbon storage: Assessment of tree diversity and above-ground biomass of homegardens in Matale district, Sri Lanka

Homegardens are principally known as integrated man-made ecosystems with annuals and perennials where trees play a significant role in storing atmospheric carbon in the vegetation as above-ground biomass (AGB). Being one of the agroforestry systems, homegardens could ease the pressure on natural forest cover in the process of carbon sequestration and carbon storage, while reducing the greenhouse gas accumulation (CO2) in the atmosphere. Thus, they could be described as a promising approach for mitigation of climatic changes. A study was conducted to assess the tree diversity and AGB carbon stock of homegardens in Matale district, Sri Lanka. A total of 122 homegardens ranging from urban dwellings up to natural eco-systems across 05 agro-ecological regions (AER) were surveyed, capturing a vast diversity. A total of 5140 woody trees were recorded from 100 genera and 45 families, covering 16.67 ha of homegardens. Six and three tree species were identified as vulnerable and near threatened, respectively in terms of national-level conservation status. Shannon-Wiener index (SWI) of 1.90 ± 0.49 ranging between 0.49 and 2.83 indicated compositional diversity of the tree species. The AGB was widely varying between 0.8 and 139.4 Mg C/ha (mean AGB of 36.5 ± 27.4 Mg C/ha). AGB and SWI were higher in small-scale (38.8 ± 29.7 Mg C/ha and 1.91 ± 0.50, respectively) than in medium-scale homegardens (28.0 ± 14.9 Mg C/ha and 1.86 ± 0.50, respectively). Species richness and number of species per hectare were higher in small scale (<0.2 ha) homegardens compared to medium scale (0.2–0.8 ha). A considerable variation of tree diversity and AGB was observed among the homegardens in different AERs. The variation of AGB was primarily governed by tree density (trees/ha) and species diversity. Focusing on that, optimizing the potential of homegardens in terms of storing atmospheric carbon as AGB in the study area can be considered as a timely strategy in mitigating impacts of climate change and assisting domestic food security.

Spatiotemporal Variation in Aboveground Biomass and Its Response to Climate Change in the Marsh of Sanjiang Plain

The Sanjiang Plain has the greatest concentration of freshwater marshes in China. Marshes in this area play a key role in adjusting the regional carbon cycle. As an important quality parameter of marsh ecosystems, vegetation aboveground biomass (AGB) is an important index for evaluating carbon stocks and carbon sequestration function. Due to a lack of in situ and long-term AGB records, the temporal and spatial changes in AGB and their contributing factors in the marsh of Sanjiang Plain remain unclear. Based on the measured AGB, normalized difference vegetation index (NDVI), and climate data, this study investigated the spatiotemporal changes in marsh AGB and the effects of climate variation on marsh AGB in the Sanjiang Plain from 2000 to 2020. Results showed that the marsh AGB density and annual maximum NDVI (NDVImax) had a strong correlation, and the AGB density could be accurately calculated from a power function equation between NDVImax and AGB density (AGB density = 643.57 × NDVI). According to the function equation, we found that the AGB density significantly increased at a rate of 2.47 g·C/m2/a during 2000–2020 in marshes of Sanjiang Plain, with the long-term average AGB density of about 282.05 g·C/m2. Spatially, the largest increasing trends of AGB were located in the north of the Sanjiang Plain, and decreasing trends were mainly found in the southeast of the study area. Regarding climate impacts, the increase in precipitation in winter could decrease the marsh AGB, and increased temperatures in July contributed to the increase in the marsh AGB in the Sanjiang Plain. This study demonstrated an effective approach for accurately estimating the marsh AGB in the Sanjiang Plain using ground-measured AGB and NDVI data. Moreover, our results highlight the importance of including monthly climate properties in modeling AGB in the marshes of the Sanjiang Plain.

Intraspecific responses of plant productivity and crop yield to experimental warming: A global synthesis

Maintaining plant productivity and crop yield in a warming world requires local adaptation to new environment and selection of high-yield cultivars, which both depend on the genetically-based intraspecific differences in the plant response to warming (referred to as “genetically-based intraspecific responses”). However, how the genetically-based intraspecific responses mediate warming effects on plants remains unclear, especially at the global scale. Here, a dataset was compiled from 118 common-garden experiments to examine the responses of plant growth, productivity, and crop yield to warming among different ecotypes/genotypes/cultivars. Our results showed that the genetically-based intraspecific responses on average accounted for 34.7 % of the total variance in the warming responses across all the studies but with large variability (2 %–77 %). The intraspecific responses of plant productivity and crop yield were larger than those of organ level traits and biomass allocation, suggesting that plant growth was mainly achieved by iterating the relatively invariant terminal modules (e.g., leaves). The warming-induced changes in intraspecific variability of aboveground biomass were larger in woody plants, non-leguminous herbs, perennial herbs and noncrops than those in nonwoody, leguminous, annual and crop ones, respectively, indicating the potential important role of plant longevity in mediating the change in intraspecific variability. Moreover, larger intraspecific responses reduced the consistence of relative performance between control and warming treatments for both plant productivity and crop yield. These results highlight the unneglectable role of genetically-based intraspecific differences in plant responses to warming, indicating the difficulty of maintaining high crop yield and tree productivity under global climate change, and posing a grave threat to the food security and wood supply in the near future.

EFFECT OF LEAF PHENOLOGY, TOPOGRAPHY AND WIND SPEED ON FOREST CANOPY HEIGHT AND ABOVE GROUND BIOMASS ESTIMATION USING OPTICAL UAV DATA IN MALAWI’S MIOMBO WOODLANDS

The study investigated the effect of tree leaf phenology, topography and wind speed on estimating forest canopy height (FCH) and above ground biomass (AGB) using optical drone data in Miombo Woodlands of Malawi. Data were acquired in 2019 from thirteen plots of Ntchisi Forest Reserve, in two-fold; tree measurements from 0.1ha in-situ plots and airborne RGB ortho-images overlaid on the terrestrial plots from 50 ha boxes. The imagery were acquired by DJI Phantom-4 Unmanned Aerial Vehicle (UAV) at ~7cm pixel resolution. These were processed using Structure-from-Motion photogrammetry algorithm in Agisoft Metashape Professional software. Malawi’s local allometry was used to estimate AGB while the Analysis of Variance was employed to test accuracy. Results reveal that leaf phenology, topography and wind speed significantly (P&lt;0.05) affected estimation of FCH and AGB using the airborne observations. Wind speed contributed highly (53.95% and 55.52%) to the total variation of FCH and AGB, respectively. Furthermore, wind speed upsurge &gt;5m.s-1 (-666.90±27.62%; -64.78±8.47%), topography &gt;15% slope (-114.53±2.92%; -17.07±2.31%) and leaf-off condition (-188.45±25.62%; -27.80±7.98 %) bore higher errors for FCH and AGB, respectively. A significant variation of relationship (R2=0.71; P&lt;0.05) was observed between ground-based height and UAV canopy height. Nevertheless, the variation of the relationship strongly increased by 10% to R2=0.81 when two plots highly affected by the wind speed were excluded from the analysis. Similarly, a significant variation of relationship (R2=0.81; P&lt;0.05) was registered between the ground-based and UAV-based AGB. Correspondingly, a very strong variation of relationship (R2= 0.95; an increase by 14%) was yielded when the two plots highly affected by wind speed were excluded from the analysis. These findings imply that seasonal leaf-fall, slope and wind speed significantly influence the accuracy of FCH and AGB estimations in Miombo Woodlands. Hence, for accurate results, it is a pre-requisite to consider leaf-phenology, topography and wind speed prior to undertaking ground and airborne inventory in the Woodlands.

Aboveground Biomass Models in the Combretum-Terminalia Woodlands of Ethiopia: Testing Species and Site Variation Effects

The Combretum-Terminalia woodlands and wooded grasslands (CTW) are widely distributed in East Africa. While these landscapes may have the potential to act as key global carbon sinks, relatively little is known about their carbon storage capacity. Here we developed a set of novel aboveground biomass (AGB) models and tested for species and site variation effects to quantify the potential for CTW to store carbon. In total, 321 trees were sampled from 13 dominant tree species, across three sites in the Northwest lowlands of Ethiopia. Overall, fitted species-specific models performed the best, with diameter at breast height explaining 94–99% of the AGB variations. Interspecific tree allometry differences among species were more substantial than intraspecific tree allometry among sites. Incorporating wood density and height in the mixed-species models significantly improved the model performance relative mean absolute error (MAPE) of 2.4–8.0%, while site variation did not affect the model accuracy substantially. Large errors (MAPE%) were observed when using existing pantropical models, indicating that model selection remains an important source of uncertainty. Although the estimates of selected site-specific models were accurate for local sites, mixed-species and species-specific models performed better when validation data collated from different sites were incorporated together. We concluded that including site- and species-level data improved model estimates of AGB for the CTW of Ethiopia.

Above-ground Biomass Wheat Estimation: Deep Learning with UAV-based RGB Images

ABSTRACT Evaluation of biomass is essential in agriculture to delineate crop management practices, and this is usually done manually, which is time-consuming and destructive. This work proposes an artificial neural network and convolutional neural network to estimate the above-ground biomass (AGB) of wheat using visible spectrum images captured by an unmanned aerial vehicle. The utilized dataset has two Brazilian wheat types, called Parrudo and Toruk. Furthermore, the experimental area has variability in crop growth by varying the amount of nitrogen. To determine AGB, samples of plants were collected at three different crop growth stages, approximately a month apart, making our database spatial and temporal variability. We have shown the feasibility of developing a regression model using RGB images for biomass estimation for two wheat types. The best results for ANN were 489.5, 826.4, and 0.9056 for MAE, RMSE, and , respectively. In CNN, MAE = 699.2, RMSE = 940.5, and = 0.9065. These results show high accuracy in estimation of biomass, and the shows good estimation and generalization capacity. The results demonstrate that our methodology can be used in precision agriculture to predict the spatial and temporal variability of AGB.

Detecting the distribution of grass aboveground biomass on a rangeland using Sentinel-2 MSI vegetation indices

Quantifying the grass biomass content of rangelands is potentially useful to rangeland managers. This study assessed the potential of image-derived vegetation indices to assess in-situ spatial variations in grass aboveground biomass (AGB). Sampling was conducted at the peak growth stage, at widely distributed sampling sites whose grass cover homogeneity was wider than 20 m. Up to three AGB samples per site were collected, in 1 m quadrats. Dry biomass weights were determined and averaged per site. Rainy season Sentinel-2 MSI images of the rangeland that corresponded with the fieldwork dates were obtained. Atmospheric correction of the images was performed using the Sen2Cor algorithm, which yielded 20 m resolution visible (vis), red edge (RE), near infrared (NIR), and short wave infrared (SWIR) bands. Seven biomass-sensitive vegetation indices that utilised Sentinel-2 MSI vis-RE-NIR-SWIR spectral ranges were then tested using the images, to determine which correlated best with the field-derived AGB. They were the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Red Edge index (NDRE), Red Edge Inflection Point (REIP), Aerosol Free Vegetation Index (AFRI 2.1μm ), and Normalized Difference Water Index (NDWI). In decreasing order of magnitude, the EVI, SAVI, NDVI, AFRI 2.1μm , and NDWI had statistically significant correlations with AGB ( p ≤ 0.05). The discontinuous canopies but high biomass of tall-grass species weakened the correlations. The EVI model of AGB was then used for depicting rangeland-scale, location-context spatial variations in AGB. Pixels with woody vegetation cover ≥ 50% were excluded using a binary mask that was developed through sub-pixel classification of a grass-senescence period Sentinel-2 MSI image. A statistically significant linear relationship ( F = 21.192, p = 0.000) between EVI-predicted and actual AGB was established ( r = 0.765, p < 0.001). The results from the studied savannah rangeland suggest higher vegetation index prediction accuracy at short-grass than tall-grass sites.

Environmental drivers of above-ground biomass in semi-arid rangelands

Above-ground biomass (AGB), as a key biophysical and functional parameter of rangeland ecosystems, plays an important role in ecosystem carbon (C) stocks. The aim of this study was to explore the important environmental drivers of AGB in an arid rangeland by using structural equation modelling (SEM). Vegetation and soil (physical and chemical properties) were sampled using randomised-systematic methods within sampling plots. The topographic (elevation, slope, aspect, hillshade (i.e. a technique for showing the topographical shape of hills and mountains to indicate relative slopes and mountain ridges)) and climatic (mean temperature (MT), mean precipitation (MP), actual evapo-transpiration (AET) and land-surface temperature (LST)) properties were extrapolated using a raster-based digital elevation-model (DEM) map, and their values extracted at each sampling plot. SEM was then applied to assess the direct and indirect impacts of environmental factors on AGB. The AGB was directly affected by soil (41%, P &lt; 0.05) and climatic properties (34%, P &lt; 0.05). The effect of topographical factors on AGB was non-significant (P &gt; 0.05). However, climatic properties were directly affected by topographical properties, with a path coefficient of 34%. Among soil properties, nitrogen, phosphorus and potassium concentrations, silt content and soil pH were the key factors affecting AGB. Climatic variables (MT, MP) were equally effective in explaining the climate latent variable (with loading factors of −0.97 and 0.99 respectively) and AGB changes. This study highlighted the importance of soil and climatic properties in AGB variation. SEM simplified and revealed the complex relationships among ecosystem components affecting AGB, which could assist proper management of rangelands.

Temporal and Spatial Variation of Aboveground Biomass of Pinus densata and Its Drivers in Shangri-La, CHINA.

Understanding the drivers of forest aboveground biomass (AGB) is essential to further understanding the forest carbon cycle. In the upper Yangtze River region, where ecosystems are incredibly fragile, the driving factors that make AGB changes differ from other regions. This study aims to investigate AGB’s spatial and temporal variation of Pinus densata in Shangri-La and decompose the direct and indirect effects of spatial attribute, climate, stand structure, and agricultural activity on AGB in Shangri-La to evaluate the degree of influence of each factor on AGB change. The continuous sample plots from National Forest Inventory (NFI) and Landsat time series were used to estimate the AGB in 1987, 1992, 1997, 2002, 2007, 2012, and 2017. The structural equation model (SEM) was used to analyze the different effects of the four factors on AGB based on five scales: entire, 1987–2002, 2007–2017, low population density, and high population density. The results are as follows: (1) The AGB of Pinus densata in Shangri-La decreased from 1987 to 2017, with the total amount falling from 9.52 million tons to 7.41 million tons, and the average AGB falling from 55.49 t/ha to 40.10 t/ha. (2) At different scales, stand structure and climate were the drivers that directly affect the AGB change. In contrast, the agricultural activity had a negative direct effect on the AGB change, and spatial attribute had a relatively small indirect effect on the AGB change. (3) Analyzing the SEM results at different scales, the change of the contribution of the agricultural activity indicates that human activity is the main negative driver of AGB change in Shangri-La, especially at the high population density region. In contrast, the change of the contribution of the stand structure and climate indicates that the loss of old trees has an important influence on the AGB change. Forest resources here and other ecologically fragile areas should be gradually restored by adhering to policies, such as strengthening forest protection, improving forest stand quality, and limiting agricultural production activities.

How can biosphere models simulate enough vegetation biomass in the mountains of the western United States? Implications of meteorological forcing

Most carbon stocks and fluxes in the western United States are found in mountainous terrain, where observations and modeling are difficult. Terrestrial biosphere models generally underestimate above-ground biomass (AGB) over this region. Here, we identify methods to reduce this underestimation by focusing upon 1) biases in meteorological datasets, 2) model representation of water stress, and 3) spatial resolution. We adopted the widely-used Community Land Model version 4.5 (CLM 4.5) with six different meteorological datasets and found a 6-fold variation in simulated AGB across Utah/Colorado. Simulations underestimated AGB because of warm and dry biases within the meteorological datasets that reduced water availability and restricted plant growth. To eliminate the AGB underestimation we adopted a meteorological dataset designed for complex terrain (gridMET), combined with a representation of plant hydraulic stress (CLM 5.0). Conversely, changes in spatial resolution (meteorological variables and land surface description) had negligible impact on simulated AGB.

Effect of Nutrient Addition on the Productivity and Species Richness of Grassland Along With an Elevational Gradient in Tajikistan.

Grasslands provide key resource for the millions of people who are dependent on livestock in Tajikistan. Productivity and species richness (SR) are important characteristics of grassland ecosystems and are greatly affected by nutrient inputs. The effect that climate change might have on these characteristics remains unclear. Here, an in situ nitrogen (N) and phosphorus (P) fertilization experiment was conducted at four sites along with an elevational gradient (650, 1,100, 1,250, and 2,000 m) in western Tajikistan over 2 years (2018 and 2019) to examine the influences of nutrient availability and climate change on aboveground biomass (AGB) and SR; precipitation and temperature were also considered to analyze the responses. It demonstrated that enrichment with N, P, and their combinations significantly increased AGB along with an elevational gradient (p < 0.05). AGB increased as the concentrations of nutrients added increased. The maximum AGB, which was 2-fold higher compared with control, was observed when 90 kg N ha–1year–1 and 30 kg P ha–1year–1 were added. In addition, nitrogen addition alone stimulated greater AGB than P addition, although no significant difference was observed between these two treatments. Enrichment with N, P, and their combination had no significant effect on SR; however, SR significantly changed at different elevation. Elevation had direct effect on precipitation and temperature, which, in turn, resulted in variation in AGB and SR. Moreover, both nutrient and elevation had significant effect on AGB and SR, but there was no interaction effect of them. AGB and SR interacted with significant negative correlation. In the high-elevation area, plants grew better in the warmer year (2018); this indicates that grasslands in high mountain areas in Tajikistan might have higher productivity as the climate warms, which will positively affect the economic development of the country.

Water Availability Controls the Biomass Increment of Melia dubia in South India

Farmland tree cultivation is considered an important option for enhancing wood production. In South India, the native leaf-deciduous tree species Melia dubia is popular for short-rotation plantations. Across a rainfall gradient from 420 to 2170 mm year–1, we studied 186 farmland woodlots between one and nine years in age. The objectives were to identify the main factors controlling aboveground biomass (AGB) and growth rates. A power-law growth model predicts an average stand-level AGB of 93.8 Mg ha–1 for nine-year-old woodlots. The resulting average annual AGB increment over the length of the rotation cycle is 10.4 Mg ha–1 year–1, which falls within the range reported for other tropical tree plantations. When expressing the parameters of the growth model as functions of management, climate and soil variables, it explains 65% of the variance in AGB. The results indicate that water availability is the main driver of the growth of M. dubia. Compared to the effects of water availability, the effects of soil nutrients are 26% to 60% smaller. We conclude that because of its high biomass accumulation rates in farm forestry, M. dubia is a promising candidate for short-rotation plantations in South India and beyond.

Functional identity regulates aboveground biomass better than trait diversity along abiotic conditions in global forest metacommunities

Although several studies have identified the effects of functional trait diversity (FTD) and/or identity, i.e. the community‐weighted mean (CWM) of a trait, on aboveground biomass (AGB) along abiotic conditions, these effects on AGB in global forest metacommunities are still largely unexplored. Here, we modelled the effects of abiotic (i.e. climate, soil and plot physical conditions) and biotic [i.e. FTD, CWM of conservative traits (CWMCT), CWM of acquisitive traits (CWMAT) and functional dominance (FunDom; based on CWM of plant maximum height or diameter)] factors on AGB in 76 forest metacommunities (from 24 studies). Using multiple linear regression models and piecewise structural equation modeling (pSEM), we tested the hypothesis that both abiotic and biotic factors regulate AGB but that the mass ratio mechanism underpins AGB of metacommunities in global forests better than the niche complementarity mechanism. We found that abiotic and biotic factors contributed 45.39% and 54.07%, respectively, to the explained variance in AGB (R2 = 0.59), and as such, abiotic factors shaped FTD (R2 = 0.42–0.48), CWMCT (R2 = 0.33–0.36), CWMAT (R2 = 0.27–0.33) and FunDom (R2 = 0.59–0.61) through divergent effect sizes and directions. The final best‐fitted pSEM showed that FunDom increased (β = 0.49) but CWMCT (β = −0.35) and CWMAT (β = −0.11) decreased AGB (R2 = 0.52) as compared to the negligible effect of FTD (β = 0.04). This study supports the mass ratio effect, specifically the overruling role of tall‐stature or dominant trees on AGB, at a macroecological scale, and hence, suggests that a suitable species' functional strategy is important to promote carbon sequestration in forest metacommunities that underpins human well‐being. We expect that our study will advance the field of biodiversity–ecosystem functioning at a macroecological scale by using the metacommunity concept and approach.

Satellite-Derived Estimation of Grassland Aboveground Biomass in the Three-River Headwaters Region of China during 1982–2018

The long-term estimation of grassland aboveground biomass (AGB) is important for grassland resource management in the Three-River Headwaters Region (TRHR) of China. Due to the lack of reliable grassland AGB datasets since the 1980s, the long-term spatiotemporal variation in grassland AGB in the TRHR remains unclear. In this study, we estimated AGB in the grassland of 209,897 km2 using advanced very high resolution radiometer (AVHRR), MODerate-resolution Imaging Spectroradiometer (MODIS), meteorological, ancillary data during 1982–2018, and 75 AGB ground observations in the growth period of 2009 in the TRHR. To enhance the spatial representativeness of ground observations, we firstly upscaled the grassland AGB using a gradient boosting regression tree (GBRT) model from ground observations to a 1 km spatial resolution via MODIS normalized difference vegetation index (NDVI), meteorological and ancillary data, and the model produced validation results with a coefficient of determination (R2) equal to 0.76, a relative mean square error (RMSE) equal to 88.8 g C m−2, and a bias equal to −1.6 g C m−2 between the ground-observed and MODIS-derived upscaled AGB. Then, we upscaled grassland AGB using the same model from a 1 km to 5 km spatial resolution via AVHRR NDVI and the same data as previously mentioned with the validation accuracy (R2 = 0.74, RMSE = 57.8 g C m−2, and bias = −0.1 g C m−2) between the MODIS-derived reference and AVHRR-derived upscaled AGB. The annual trend of grassland AGB in the TRHR increased by 0.37 g C m−2 (p &lt; 0.05) on average per year during 1982–2018, which was mainly caused by vegetation greening and increased precipitation. This study provided reliable long-term (1982–2018) grassland AGB datasets to monitor the spatiotemporal variation in grassland AGB in the TRHR.

The interaction of land-use history and tree species diversity in driving variation in the aboveground biomass of urban versus non-urban tropical forests

Understanding the drivers of aboveground biomass (AGB) variation in present-day tropical forests can contribute to management strategies that help mitigate against CO2-driven climate change and provide other services related to high AGB. Higher tree diversity can lead to higher woody productivity and carbon storage, but how diversity interacts with land-use history is less certain. We assessed variation in AGB across forests with different land-use histories and surrounding landscapes in southeastern Brazil and how AGB relates to tree diversity per se, while controlling for important factors such as mean functional trait values, stem density and soil fertility. Our findings indicate that aboveground biomass of forests is dependent on land-use history and the landscape matrix in which they occur (urban or non-urban). We found that tree diversity, measured as the average evolutionary divergence among close relatives, shows a strong positive relationship to AGB, but only in old-growth, non-urban forests. This suggests that higher niche complementarity leads to higher AGB in certain ecological contexts. Forests in an urban matrix, and those that regenerated from cropland (in an urban or non-urban matrix), showed weak or insignificant relationships between AGB and diversity, and forests that regenerated from completely denuded landscapes, including soil removal, actually showed a negative relationship between diversity and AGB. Meanwhile, across all forest classes, the abundance-weighted mean wood density of tree species present showed a consistent positive correlation with AGB, indicating the ubiquity of mass-ratio effects on AGB. Overall, our study suggests that strategies for conservation and restoration should account for past land-use and the matrix where forests are inserted, as the distribution of carbon stocks and biodiversity may need to be considered separately.

Estimating Above-Ground Biomass of Potato Using Random Forest and Optimized Hyperspectral Indices

Spectral indices rarely show consistency in estimating crop traits across growth stages; thus, it is critical to simultaneously evaluate a group of spectral variables and select the most informative spectral indices for retrieving crop traits. The objective of this study was to explore the optimal spectral predictors for above-ground biomass (AGB) by applying Random Forest (RF) on three types of spectral predictors: the full spectrum, published spectral indices (Pub-SIs), and optimized spectral indices (Opt-SIs). Canopy hyperspectral reflectance of potato plants, treated with seven nitrogen (N) rates, was obtained during the tuber formation and tuber bulking from 2015 to 2016. Twelve Pub-SIs were selected, and their spectral bands were optimized using band optimization algorithms. Results showed that the Opt-SIs were the best input variables of RF models. Compared to the best empirical model based on Opt-SIs, the Opt-SIs based RF model improved the prediction of AGB, with R2 increased by 6%, 10%, and 16% at the tuber formation, tuber bulking, and for across the two growth stages, respectively. The Opt-SIs can significantly reduce the number of input variables. The optimized Blue nitrogen index (Opt-BNI) and Modified red-edge normalized difference vegetation index (Opt-mND705) combined with an RF model showed the best performance in estimating potato AGB at the tuber formation stage (R2 = 0.88). In the tuber bulking stage, only using optimized Nitrogen planar domain index (Opt-NPDI) as the input variable of the RF model produced satisfactory accuracy in training and testing datasets, with the R2, RMSE, and RE being 0.92, 208.6 kg/ha, and 10.3%, respectively. The Opt-BNI and Double-peak nitrogen index (Opt-NDDA) coupling with an RF model explained 86% of the variations in potato AGB, with the lowest RMSE (262.9 kg/ha) and RE (14.8%) across two growth stages. This study shows that combining the Opt-SIs and RF can greatly enhance the prediction accuracy for crop AGB while significantly reduces collinearity and redundancies of spectral data.

Spatiotemporal change of aboveground biomass and its response to climate change in marshes of the Tibetan Plateau

The Tibetan Plateau has a large area of marshes which play a crucial part in the global carbon cycle. The vegetation aboveground biomass (AGB) is a critical indicator of carbon storage of marsh ecosystems. Due to the limitations of field investigation, the spatiotemporal variation of AGB and its influencing factors of the Tibetan Plateau marshes remain unclear. By using the normalized difference vegetation index (NDVI), climate and measured AGB data, this study investigated the temporal and spatial variation of marsh AGB and its response to climate change in the Tibetan Plateau during 2000–2019. The results showed a good correlation between AGB and annual maximum NDVI (NDVImax), and the AGB can be accurately estimated from a power function equation between marshes biomass and NDVImax (Y = 343.08 × NDVImax0.7363). Based on the function equation, we found that AGB density increased significantly (4.10 g·C/m2/decade) from 2000 to 2019 over the Tibetan Plateau, with an average value of 184.71 g·C/m2. In terms of climate effects, increased precipitation in July could significantly increase marsh AGB, while precipitation in other months has no significant effect on the marsh AGB in the Tibetan Plateau. This study first found an asymmetric impact of night and day temperature on AGB in the Tibetan Plateau marshes: warming day-time temperature has no-significant effect on marsh AGB, while night-time warming can significantly increase the AGB of marshes. Considering the background of global asymmetric warming in nighttime and daytime, more attention should be paid to the different impacts of day and night temperatures on marsh AGB in the Tibetan Plateau.

Scaled biomass estimation in woodland ecosystems: Testing the individual and combined capacities of satellite multispectral and lidar data

Airborne laser scanning (ALS) data enable accurate modeling and mapping of aboveground biomass (AGB), but the limited spatial and temporal extents of ALS data collection limit the capacity for broad-scale carbon accounting. Conversely, while space-based remote sensing instruments provide increased spatial and temporal coverage, it can be difficult to directly link field-level vegetation biometrics to satellite data due to coarser spatial resolution and positional uncertainty. The combined use of ALS and satellite remote sensing data may offer a solution to efficient, accurate, and consistent AGB mapping across time and space. Such airborne-spaceborne data fusion has been demonstrated successfully in high-biomass settings; however, the unique structural conditions of dryland woodland ecosystems, with open canopies and low leaf area indices, pose mapping challenges that require further study. These challenges are particularly acute with large footprint spaceborne lidar, where short, widely-spaced trees may limit the capacity for accurate AGB estimation. In this study, we present a scaled methodological framework for linking field-measured woodland AGB to ALS data and, in turn, linking ALS-modeled AGB to satellite data, using piñon-juniper woodlands in southeastern Utah as a case study. We compare the effectiveness of this scaling approach using two satellite sensors, Landsat 8 OLI (multispectral) and GEDI (lidar). Since the predicted outputs of our local-scale model are being used as inputs to our regional-scale model, we also demonstrate an approach for propagating uncertainty throughout this nested, multiscale analytical framework, leveraging the inherent variability within a random forest's decision trees. Given the positional uncertainty of GEDI footprints, we test a range of different footprint sizes for their relative effects on ALS-GEDI AGB model accuracy. Our local-scale (field-ALS) predictive model was able to account for 74% of variance in AGB, and estimate AGB with a root mean squared error (RMSE) of 14 Mg/ha, a mean absolute error (MAE) of 11.09 Mg/ha. Our regional-scale (ALS-Landsat/GEDI) analysis with propagated uncertainty revealed that the combined use of Landsat and GEDI metrics produced the best predictive model (R2 = 0.68; RMSE = 12.71 Mg/ha; MAE = 9.40 Mg/ha), followed by Landsat-only metrics (R2 = 0.66, RMSE = 13.08 Mg/ha; MAE = 9.71 Mg/ha), and GEDI-only metrics (R2 = 0.49, RMSE = 16.01 Mg/ha; MAE = 12.14 Mg/ha). These results suggest that Landsat may be better-suited than GEDI for estimating AGB in woodland environments where low canopy covers and short trees limit the capacity for precisely characterizing vegetation structure within large-footprint, waveform lidar data. The footprint size analysis revealed that larger simulated footprints (e.g., 30 m radius and greater) produced higher GEDI model accuracies; however, increasing footprint radii beyond 30 m does not significantly increase model accuracy. This research represents an important step forward in improving our capacity for reliably mapping woodland AGB, and provides an early test case for the application of GEDI data to woodland AGB mapping.