Large Aboveground Biomass Research Articles

Improving treatment performance in mature wetlands: The impact of emergent plant species

Improving the treatment performance of mature surface flow constructed wetlands (SFCWs) is critical for long-term sustainability. Nevertheless, the mechanisms of plant species affecting the treatment performance of mature SFCWs remain largely unexplored. In this study, three 6-year-old field-scale SFCWs were subjected to orthogonal tests, with Nelumbo nucifera, Typha angustifolia, and Zizania latifolia as plant species. The aim was to understand how the emergent plant species selected for the newly constructed wetlands influenced the nitrogen and phosphorus treatment performance at maturity. The results showed that plant species significantly affected total nitrogen (TN) treatment performance through dissolved oxygen and water temperature. Typha and Zizania had larger above-ground biomass and denser canopies than Nelumbo, significantly increasing anaerobic zones and water temperature. Therefore, SFCWs with Typha or Zizania had the best organic nitrogen and nitrate nitrogen treatment performance compared to those with Nelumbo. However, the integrated ability of microbes and plants to remove ammonia nitrogen was not affected by internal factors in mature wetlands. Plant species significantly affected total phosphorus (TP) treatment performance through local flow patterns. Although plant species did not significantly affect general hydraulic performance, Zizania had denser tillers and thicker stalks than Nelumbo and Typha, increasing flow velocity near the wetland bottom. Therefore, SFCWs with Nelumbo or Typha were more capable of TP removal than those with Zizania. Overall, Typha was the optimal plant species compared to Nelumbo and Zizania for efficient removal of TN and TP. Therefore, appropriate plant species for newly constructed wetlands are crucial for enhancing nitrogen and phosphorus treatment performance at maturity. From a long-term perspective, SFCWs should be planted with emergent plants with large above-ground parts and sparse tillers. This study provides a mechanistic understanding of the plant species affecting nitrogen and phosphorus in mature SFCWs.

Post-heading accumulation of nonstructural carbohydrates and nitrogen in rice (Oryza sativa L.) roots

ContextRoots are plant organs that absorb nutrients and water. While roots serve as sink organs for nutrients in perennial and woody plant species, the dynamics of nutrient accumulation in roots for annual plant species, particularly in crop species like Oryza sativa L. (rice), are not well understood. Japanese rice cultivars bred for whole-crop silage exhibit large aboveground biomass and a notably small number of spikelets. This sink-source imbalance leads to the accumulation of non-structural carbohydrates (NSC) in stems after heading. Research questionIt is hypothesized that rice cultivars with substantial aboveground biomass might also develop significant belowground biomass. Consequently, the sink-source imbalance may result in NSC accumulation in roots. This study aims to test two hypotheses: firstly, that newly developed small-panicle rice cultivars accumulate NSC in roots post-heading, and secondly, that this post-heading NSC accumulation in roots can be influenced by cultural practices affecting spikelet number per panicle. MethodsTwo small-panicle cultivars, Tachisuzuka and Tachiayaka, and two large-panicle cultivars, Kusanohoshi and Hoshiaoba, were grown using different methods of three cultural practices: nitrogen application, transplanting season, and planting density. Measurements of dry matter, NSC (starch and sugars), and nitrogen in each plant part (panicle, leaf, stem, and roots) were taken over time after heading. The spikelet number per panicle was also counted. ResultsPost-heading, the small-panicle cultivars exhibited NSC accumulation in roots, a phenomenon not observed in the large-panicle cultivars. Although the three cultural practices altered the spikelet number per panicle and thus the sink capacity of the small-panicle cultivars, root NSC accumulation remained unaffected. Additionally, nitrogen weight per unit field area increased with increases in root weight in the small-panicle cultivars. ConclusionsThe imbalance in the sink-source relationship is likely responsible for the post-heading NSC accumulation in roots observed in small-panicle cultivars. However, the lack of root response in NSC accumulation to changes in panicle sink capacity suggests other mechanisms might be involved in root NSC accumulation. Notably, significant amounts of NSC and nitrogen remain in the roots of small-panicle cultivars after harvest. ImplicationsThe accumulation of NSC and nitrogen in roots post-heading could potentially enhance crop growth in subsequent years.

Consistent geographical gradient of water use efficiency evidences local adaptations to drought across the complete latitudinal distribution of Quercus suber

RationaleIncreased aridity has led to drought-induced mortality or loss of health for many tree species. Of particular interest is to explore the response of the Mediterranean tree species cork oak (Quercus suber) to this declining phenomenon due to its severity and its large implications for the local economy and the provision of highly relevant ecosystem services. ApproachTo assess geographical variations in the response to water stress, we analyzed under controlled conditions the effects of four watering levels and the resistance to a terminal drought on seedlings collected from nine populations covering the complete latitudinal distribution of the species. We explored the response of a number of physiological traits and markers of oxidative stress potentially related with drought-resistance. FindingsWe found a highly plastic phenotypic response of most variables to water availability and a large influence of seedlings provenance in the drought-resistance strategies. Although the oxidative stress enzymes ruled out differential water stress throughout the distribution range, we found that seedlings from the southern limit are less vulnerable to drought than other populations. Southern seedlings adapted to xeric conditions displayed a larger sensitivity of stomata to changes in soil humidity and a higher water use efficiency. These physiological local adaptations coupled with larger acorn size in the southern populations, resulted in larger aboveground biomass and higher drought resistance at the southern distribution edge. ConclusionsOur data suggest that this evergreen tree species relies on its physiological plasticity to develop adaptative features that allows it to overcome water shortage and that southern populations hold genetic diversity that could improve the specie´s adaptation in the forecasted drought context with relevant implications for conservation programs.

Composition of aboveground phytomass of subalpine fens in Teberdinsky National Park

The composition of the aboveground phytomass of unique subalpine plant community Teberdinsky National Park – the subalpine fens – has been studied. The aboveground vascular plant biomass averaged 264±12 g/m2 , the species of Cyperaceae prevailed (41%), the role of forbs (37%) and grasses (21%) is smaller, the remaining groups of vascular plants and lichens are very rare. Bryophytes are abundant (31,2% of total aboveground phytomass). Communities have polydominant structure, the largest aboveground biomass is formed by Carex nigra (26,1%), Nardus stricta (15,8%), Cirsium simplex (13,4%), Primula auriculata (6,7%), Blysmus compressus (5,2%), Swertia iberica (5,2%). Mortmass store is relatively small (168±12 g/m2 ), which indicates a high rate of decomposition and an intensive biological cycle.

Consistent yield performance of rice genotypes grown under irrigated conditions in wet and dry seasons in West Africa

ContextRice double cropping in irrigated systems is essential for increasing rice production and ensuring food security in West Africa. Rice breeding programs must develop genotypes that are expected to perform well either across wet and dry seasons or for a specific season. ObjectiveThis study aimed to investigate the consistency of genotypic performance across two seasons and identify high-yielding genotypes and their characteristics. Methods50 diverse genotypes were grown under irrigated conditions in wet and dry seasons at three sites in West Africa (M’bé in Côte d’Ivoire, and Fanaye and Ndiaye in Senegal) for three years (2018–2020). ResultsMean grain yield across all genotypes was higher in dry season in Fanaye (593 vs. 495 g/m2) and Ndiaye (737 vs. 415 g/m2) than in wet season, but the same for the two seasons in M’bé (512 vs. 507 g/m2). The consistency of genotypic performance across wet and dry seasons was the highest in M’bé (r = 0.88, P < 0.01), followed by Fanaye (r = 0.71, P < 0.01), and lowest in Ndiaye (r = 0.49, P < 0.01). Thus, correlations for yields between two seasons becomes poorer as difference in yield level between two seasons becomes larger. Two (NSIC Rc240 and Giza 178) and one (MG 2::IRGC 79837-1) genotypes from other regions were identified for high yield in both seasons in Côte d’Ivoire and Senegal, respectively. High-yielding genotypes were characterized by large aboveground biomass and high harvest index in both seasons in all three sites. Grain yield was positively correlated with days to heading and number of grains per panicle in both seasons in M’bé and Fanaye. In Ndiaye, none of the yield components was consistently related to grain yield across two seasons. ConclusionsIt is possible for rice breeding programs to develop and identify genotypes that are expected to perform well in two seasons. High-yielding genotypes identified in this study can be used as donors for improving rice productivity in West Africa. ImplicationsField experiments were conducted in three sites in two countries only, and further effort is needed to evaluate consistency of genotypic performance across diverse agro-ecological zones in West Africa.

Drivers of aboveground biomass shift with forest stratum in temperate forest of North China

A better understanding of the underlying ecological mechanisms of diversity-biomass relationships in forest layers (i.e., overstory and understory) is critical to understand the importance of vertical stratification to the functioning of forest ecosystems. However, it is not clear how multiple abiotic (i.e., climate and geography) and biological (i.e., biodiversity, functional characteristics, and stand structural complexity) factors simultaneously determine the aboveground biomass (AGB) of each individual forest stratum. We used data on 156,270 trees from 1986 plots in North China to explore the relationships among biological diversity, plant functional traits, stand structure, climate and topography on variation in AGB of each stratum. The results showed that different biological factors determined the AGB of overstory and understory, and thus indicating different underlying ecological mechanisms in temperate forests. The effects of forest biodiversity on AGB were only significant in understory stratum. In the overstory of the forest, forests with high tree-size dimension inequality and high dominant tree height had larger AGB, hence mass ratio effect and stand structure complexity were the main ecological mechanisms for high biomass. In understory, diversity and overstory attributes were the main factors affecting biomass. Tree height and AGB of the overstory reduced the AGB of the understory layer. In consequence overstory attributes and niche complementation were the main ecological mechanisms in the understory. The overstory exerted influence on the understory through resource quantity and resource heterogeneity. Our findings have important implications for carbon management, enhancement of forest functions and sustainable forest management in temperate forests.

Inter- and intra-event rainfall partitioning dynamics of two typical xerophytic shrubs in the Loess Plateau of China

Abstract. Rainfall is known as the main water replenishment in dryland ecosystems, and rainfall partitioning by vegetation reshapes the spatial and temporal distribution patterns of rainwater entry into the soil. The dynamics of rainfall partitioning have been extensively studied at the inter-event scale, yet very few studies have explored its finer intra-event dynamics and the relating driving factors for shrubs. Here, we conducted a concurrent in-depth investigation of all rainfall partitioning components at inter- and intra-event scales for two typical xerophytic shrubs (Caragana korshinskii and Salix psammophila) in the Liudaogou catchment of the Loess Plateau, China. The event throughfall (TF), stemflow (SF), and interception loss (IC), and their temporal variations within the rainfall event, as well as the meteorological factors and vegetation characteristics, were systematically measured during the 2014–2015 rainy seasons. Our results showed that C. korshinskii had significantly higher SF percentage (9.2 %) and lower IC percentage (21.4 %) compared to S. psammophila (3.8 % and 29.5 %, respectively), but their TF percentages were not significantly different (69.4 % vs. 66.7 %). At the intra-event scale, TF and SF of S. psammophila were initiated (0.1 vs. 0.3 h and 0.7 vs. 0.8 h) and peaked (1.8 vs. 2.0 h and 2.1 vs. 2.2 h) more quickly, and TF of S. psammophila lasted longer (5.2 vs. 4.8 h) and delivered more intensely (4.3 vs. 3.8 mm h−1), whereas SF of C. korshinskii lasted longer (4.6 vs. 4.1 h) and delivered more intensely (753.8 vs. 471.2 mm h−1). For both shrubs, rainfall amount was the most significant factor influencing inter-event rainfall partitioning, and rainfall intensity and duration controlled the intra-event TF and SF variables. The C. korshinskii with larger branch angle, more small branches, and smaller canopy area, has an advantage over S. psammophila to produce SF more efficiently. The S. psammophila has lower canopy water storage capacity to generate and peak TF and SF earlier, and it has larger aboveground biomass and total canopy water storage of individual plants to produce higher IC compared to C. korshinskii. These findings contribute to the fine characterization of shrub-dominated ecohydrological processes, and improve the accuracy of water balance estimation in dryland ecosystems.

Large Area Aboveground Biomass and Carbon Stock Mapping in Woodlands in Mozambique with L-Band Radar: Improving Accuracy by Accounting for Soil Moisture Effects Using the Water Cloud Model

Soil moisture effects limit radar-based aboveground biomass carbon (AGBC) prediction accuracy as well as lead to stripes between adjacent paths in regional mosaics due to varying soil moisture conditions on different acquisition dates. In this study, we utilised the semi-empirical water cloud model (WCM) to account for backscattering from soil moisture in AGBC retrieval from L-band radar imagery in central Mozambique, where woodland ecosystems dominate. Cross-validation results suggest that (1) the standard WCM effectively accounts for soil moisture effects, especially for areas with AGBC ≤ 20 tC/ha, and (2) the standard WCM significantly improved the quality of regional AGBC mosaics by reducing the stripes between adjacent paths caused by the difference in soil moisture conditions between different acquisition dates. By applying the standard WCM, the difference in mean predicted AGBC for the tested path with the largest soil moisture difference was reduced by 18.6%. The WCM is a valuable tool for AGBC mapping by reducing prediction uncertainties and striping effects in regional mosaics, especially in low-biomass areas including African woodlands and other woodland and savanna regions. It is repeatable for recent L-band data including ALOS-2 PALSAR-2, and upcoming SAOCOM and NISAR data.

Large tree mortality leads to major aboveground biomass decline in a tropical forest reserve.

Humans are transforming the ecology of the Earth through rapid changes in land use and climate. These changes can affect tropical forest structure, dynamics and diversity. While numerous studies have focused on diversity metrics, other aspects of forest function, such as long-term biomass dynamics, are often less considered. We evaluated plant community structure change (i.e., abundance, diversity, composition, and aboveground biomass) in a 2.25ha forest dynamics plot located within a ~ 365ha reserve in southern Costa Rica. We censused, mapped and identified to species all plants ≥ 5cm diameter at breast height (DBH) in three surveys spanning 2010-2020. While there were no changes in late-successional species diversity, there were marked changes in overall species composition and biomass. Abundance of large (≥ 40cm DBH) old-growth dense-wooded trees (e.g., Lauraceae, Rosaceae) decreased dramatically (27%), leading to major biomass decline over time, possibly driven by recent and recurrent drought events. Gaps created by large trees were colonized by early-successional species, but these recruits did not make up for the biomass lost. Finally, stem abundance increased by 20%, driven by increasing dominance of Hampea appendiculata. While results suggest this reserve may effectively conserve overall plant diversity, this may mask other key shifts such as large aboveground biomass loss. If this pattern is pervasive across tropical forest reserves, it could hamper efforts to preserve forest structure and ecosystem services (e.g., carbon storage). Monitoring programs could better assess carbon trends in reserves over time simply by tracking large tree dynamics.

Feasibility of using divergent plantation to aggrandize the pollutants abatement from sewage and biomass production in treatment wetlands

Constructed wetlands are one of the most viable wastewater treatment technologies, with an additional aspect of biomass production that could be utilized in the manufacturing of valuable products. With this objective, four species of divergent commercial plants were grown individually and polyculture in horizontal subsurface flow constructed wetlands (HSFCW). Rural sewage was supplied from a storage tank to each CW through the inlet chamber. The experiment was continued for 12 weeks (July to October). Collectively, the HSFCW having polyculture of plants was more effective in the removal of all pollutants. While monoculture of Ipomoea aquatica exhibited better performance for the reduction of chemical oxygen demand (COD) and nutrients, viz., total nitrogen (TN), total phosphorus (TP), ammonium nitrogen (NH4+–N), and nitrate-nitrogen (NO3̄–N) with a removal efficiency of 69, 77, 96, 90 and 80%, respectively. Whereas, Iris pseudacorus and Canna indica showed significantly better results for fecal coliform and total suspended solids reduction. At the last stage of the experiment, all plant species achieved the maximum growth rate; particularly, Ipomoea aquatica and polyculture or mixed planting (MP) had the highest growth rate and larger above-ground biomass. Thus, current findings reflect that MP wetland in wastewater treatment coupled with biomass production renders environmental and economic benefits.

Effects of western juniper (Juniperus occidentalis) control on ecosystem nitrogen stocks in central Oregon, USA

Abstract Aims In the Oregon of USA, the control of western juniper (Juniperus occidentalis) is an accepted rangeland management practice to restore sagebrush steppe habitats of importance to wildlife and livestock. The effects of juniper cutting on ecosystem nitrogen, however, have not been well addressed although woody plant control has important implications for local watershed management and regional nitrogen pools. Methods We quantified ecosystem nitrogen stocks in two adjacent watersheds, comprised of a treated watershed (most juniper removed) and an untreated watershed (juniper not removed). Thirteen years after juniper removal, we measured aboveground nitrogen stocks for juniper trees, shrubs, grasses and litter in both watersheds. We also measured belowground nitrogen stocks (roots and soil) in both watersheds at two soil depths (0–25 and 25–50 cm). Important Findings Aboveground nitrogen stocks were 6.9 times greater in the untreated than in the treated watershed considering the much larger aboveground biomass. However, root nitrogen stocks were 3.1 times greater in the treated one due to the gain of understory root biomass associated with juniper cutting. Soil nitrogen stocks at both 0–25 and 25–50 cm depths were not affected by juniper removal. Overall, total ecosystem nitrogen stocks did not differ between the treated (9536 kg N ha−1) and untreated (9456 kg N ha−1) watersheds. The greatest ecosystem nitrogen accumulation (at least 95% total ecosystem nitrogen) resided belowground (soil 0–50 cm and roots) in both watersheds. This study provides evidence that the benefits of juniper removal can be attained without significantly affecting the capacity of ecosystem nitrogen storage.

The real potential of current passive satellite data to map aboveground biomass in tropical forests

AbstractForest biomass estimation at large scale is challenging and generally entails large uncertainty in tropical regions. With their wall‐to‐wall coverage ability, passive remote sensing signals are frequently used to extrapolate field estimates of forest aboveground biomass (AGB). However, studies often use limited reference data and/or flawed validation schemes and thus report unreliable extrapolation error estimates. Here, we compared the ability of three medium‐ to high‐resolution passive satellite sensors, Landsat‐8 (L8), Sentinel‐2B (S2) and Worldview‐3 (WV3), to map AGB in a forest landscape of Thailand. We used a large airborne LiDAR‐derived AGB dataset as a reference to train and validate a random forest algorithm and conducted robust error assessments and variable selection using spatialized cross‐validations. Our results indicate that the selected predictors strongly varied among the three sensors and between analyses restricted to low (≤200 Mg ha−1) and high (&gt;200 Mg ha−1) AGB areas. WV3 and S2 data outperformed L8 data to extrapolate AGB (RMSE of 68 and 72 against 84 Mg ha−1, respectively) due to the inclusion of the red‐edge band and, probably, to their higher spatial and spectral resolution. Sensitivity to large AGB values was higher for WV3 than for S2 and L8 with saturation point of 247 Mg ha−1 against 204 and 192 Mg ha−1. AGB values above these saturation points remained poorly predictable, especially for L8, indicating that several tropical forest AGB maps should be interpreted with extreme caution. However, predicted gradients of lower AGB values (≤200 Mg ha−1), i.e., in early forest successional stages, were fairly consistent among sensors (r &gt; 0.70), even if the mean absolute difference between estimates was large when AGB predictions were extrapolated out of the calibration area at regional level (34%). We finally showed that calibrating the model only within the sensitivity AGB domain (e.g., &lt;200 Mg ha−1) minimizes the risk of induced bias for estimating small AGB values. These results provide important benchmarks for interpreting previously published maps and to improve future validation schemes.

Selection of resistant species of indoor plants for the health improvement the air environment of interiors

Our studies show that at low illuminance, close to the light compensation point, plants of all studied species (Plectranthus amboinicus (Lour.) Spreng, Rhoicissus rhomboidea (E. Mey. ex Harv.) Planch., Elettaria cardamomum (L.) Maton) have a smaller total leaves area and low decorative qualities, and they have not antimicrobial activity. Plants grown under illuminance from 500 to 2000 lux, depending on the species, form a large above-ground biomass, live several times longer, have a pronounced phytoncidal effect (antimicrobal activity) on air microflora and Staphylococcus aureus. The maximum phytoncidal activity of E. cardamomum, P. amboinicus and R. rhomboidea are observed at an illuminance of 2000 lux (respectively 29 %, 36 % and 24 %). Phytoncidal activity decreases 2-3 times with a decline in the illuminance level to 500 lux, and in Indian borage, as a more light-loving plant, it disappears altogether. Phytoncidal activity of P. amboinicus(Indian borage) is 32% in relation to Staphylococcus aureus strain 209- P. Phytocompositions from resistant species of indoor plants reduce the number of microorganisms in the air of classrooms of secondary school by 45% -58%, and ones of primary school - by 5.2-7.3 times.

Mapping above-ground biomass in tropical forests with ground-cancelled P-band SAR and limited reference data

This paper introduces the CASINO (CAnopy backscatter estimation, Subsampling, and Inhibited Nonlinear Optimisation) algorithm for above-ground biomass (AGB) estimation in tropical forests using P-band (435 MHz) synthetic aperture radar (SAR) data. The algorithm has been implemented in a prototype processor for European Space Agency's (ESA's) 7th Earth Explorer Mission BIOMASS, scheduled for launch in 2023. CASINO employs an interferometric ground cancellation technique to estimate canopy backscatter (CB) intensity. A power law model (PLM) is then used to model the dependence of CB on AGB for a large number of systematically distributed SAR data samples and a small number of calibration areas with a known AGB. The PLM parameters and AGB for the samples are estimated simultaneously within pre-defined intervals using nonlinear minimisation of a cost function. The performance of CASINO is assessed over six tropical forest sites on two continents: two in French Guiana, South America and four in Gabon, Africa, using SAR data acquired during airborne ESA campaigns and processed to simulate BIOMASS acquisitions. Multiple tests with only two randomly selected calibration areas with AGB > 100 t/ha are conducted to assess AGB estimation performance given limited reference data. At 2.25 ha scale and using a single flight heading, the root-mean-square difference (RMSD) is ≤ 27% for at least 50% of all tests in each test site and using as reference AGB maps derived from airborne laser scanning data. An improvement is observed when two flight headings are used in combination. The most consistent AGB estimation (lowest RMSD variation across different calibration sets) is observed for test sites with a large AGB interval and average AGB around 200–250 t/ha. The most challenging conditions are in areas with AGB < 200 t/ha and large topographic variations. A comparison with 142 1 ha plots distributed across all six test sites and with AGB estimated from in situ measurements gives an RMSD of 20% (66 t/ha).

Ecosystem‐scale carbon allocation among different land uses: implications for carbon stocks in the Yellow River Delta

AbstractThe reclamation area of the Chinese Yellow River Delta (YRD) has experienced frequent land use changes in recent decades. The consequence of such land use changes on the stocks and allocation of ecosystem‐scale carbon is not known. Here, we assessed carbon stocks and allocation of four representative land uses in the YRD area: (1) purple alfalfa (LAL), (2) reed and Aeluropus littoralis (RAE), (3) cotton (ECO), and (4) Chinese tamarisk (CTA). The results showed that the overall carbon stocks, and carbon stocks of aboveground, litter, roots, and soil were notably different among different land uses. The native CTA land had the largest overall carbon stock (belowground and aboveground) and had the strongest potential to allocate the carbon to the soil carbon pool (95.72%), followed by the natural grassland (RAE). Alfalfa grassland (LAL) also had a large carbon stock due to its large aboveground biomass, litter, and roots, but the relative allocation proportion of soil carbon stock was lower than that of the other land uses examined. Cotton (ECO) had the lowest soil carbon and total carbon stocks among four land uses. In combining our data on the carbon pool with changes of land use in the YRD area, we argued that land reclamation in the YRD area was likely to turn this area from a carbon sink to carbon source with the release of soil organic carbon. Therefore, cautions should be taken to reduce carbon release, if the reclaimed land be used to plant crops.

Asymmetric interactions of seed-predation network contribute to rare-species advantage.

Although the asymmetry of species linkage within ecological networks is now well recognized, its effect on communities has scarcely been empirically investigated. Based on theory, we predicted that an asymmetric architecture of antagonistic plant-herbivore networks would emerge at the community level and that this asymmetry would negatively affect community-common plants more than rare ones. We tested this prediction by analyzing the architectural properties of an alpine plant and pre-dispersal seed-predator network and its effect on seed loss rate of plants in the Tibetan Plateau. This network showed an asymmetric architecture, where the common plant species (with a larger aboveground biomass per area) were infested by a higher number of predator species. Moreover, they asymmetrically interacted with specialized herbivores, presumably because of greater seed resource abundance. In turn, the asymmetric interactions led to a higher proportion of seed loss in the common plants at the species level. Our results suggest that asymmetric antagonistic networks may improve species coexistence by contributing to a mechanism of rare-species advantage.

Improving Forest Aboveground Biomass Estimation of Pinus densata Forest in Yunnan of Southwest China by Spatial Regression using Landsat 8 Images

Uncertainties in forest aboveground biomass (AGB) estimates resulting from over- and underestimations using remote sensing data have been widely studied. The uncertainties may occur due to the spatial effects of the plot data. In this study, we collected AGB data from a total of 147 Pinus densata forest sample plots in Yunnan of southwestern China and analyzed the spatial effects on the estimation of AGB. An ordinary least squares (OLS) and four spatial regression methods were compared for the estimation using Landsat 8-OLI images. Through the spatial analysis of AGB and residuals of model predictions, it was found that the spatial autocorrelation and heterogeneity of the plot data could not be ignored. Compared with the OLS, the impact of the spatial effects on AGB estimation could be reduced slightly by the spatial lag model (SLM) and the spatial error model (SEM) and greatly reduced by the linear mixed effects model (LMM) and geographically weighted regression (GWR) based on the distributions of prediction residuals, global Moran’s I, and Z score. The spatial regression models had better performance for model fitting and prediction because of the reduction in overestimations and underestimations for the forests with small and large AGB values, respectively. However, the reductions in the overestimations and underestimations varied depending on the spatial regression models. The GWR provided the most accurate predictions with the largest R2 (0.665), the smallest root mean square error (34.507), and mean relative error (−9.070%) by greatly reducing the AGB interval for overestimations occurring and significantly increasing the threshold of AGB from 150 Mg/ha to 200 Mg/ha for underestimations. Thus, GWR offered the greatest potential of improving the estimation of Pinus densata forest AGB in Yunnan of southwestern China.

Estimation of Above Ground Biomass in a Tropical Mountain Forest in Southern Ecuador Using Airborne LiDAR Data

A reliable estimation of Above Ground Biomass (AGB) in Tropical Mountain Forest (TMF) is still complicated, due to fast-changing climate and topographic conditions, which modifies the forest structure within fine scales. The variations in vertical and horizontal forest structure are hardly detectable by small field plots, especially in natural TMF due to the high tree diversity and the inaccessibility of remote areas. Therefore, the present approach used remotely sensed data from a Light Detection and Ranging (LiDAR) sensor in combination with field measurements to estimate AGB accurately for a catchment in the Andes of south-eastern Ecuador. From the LiDAR data, information about horizontal and vertical structure of the TMF could be derived and the vegetation at tree level classified, differentiated between the prevailing forest types (ravine forest, ridge forest and Elfin Forest). Furthermore, topographical variables (Topographic Position Index, TPI; Morphometric Protection Index, MPI) were calculated by means of the high-resolution LiDAR data to analyse the AGB distribution within the catchment. The field measurements included different tree parameters of the species present in the plots, which were used to determine the local mean Wood Density (WD) as well as the specific height-diameter relationship to calculate AGB, applying regional scale modelling at tree level. The results confirmed that field plot measurements alone cannot capture completely the forest structure in TMF but in combination with high resolution LiDAR data, applying a classification at tree level, the AGB amount (Mg ha−1) and its distribution in the entire catchment could be estimated adequately (model accuracy at tree level: R2 &gt; 0.91). It was found that the AGB distribution is strongly related to ridges and depressions (TPI) and to the protection of the site (MPI), because high AGB was also detected at higher elevations (up to 196.6 Mg ha−1, above 2700 m), if the site is situated in depressions (ravine forest) and protected by the surrounding terrain. In general, highest AGB is stored in the protected ravine TMF parts, also at higher elevations, which could only be detected by means of the remote sensed data in high resolution, because most of these areas are inaccessible. Other vegetation units, present in the study catchment (pasture and subpáramo) do not contain large AGB stocks, which underlines the importance of intact natural forest stands.

Comparative Analysis of Modeling Algorithms for Forest Aboveground Biomass Estimation in a Subtropical Region

Remote sensing–based forest aboveground biomass (AGB) estimation has been extensively explored in the past three decades, but how to effectively combine different sensor data and modeling algorithms is still poorly understood. This research conducted a comparative analysis of different datasets (e.g., Landsat Thematic Mapper (TM), ALOS PALSAR L-band data, and their combinations) and modeling algorithms (e.g., artificial neural network (ANN), support vector regression (SVR), Random Forest (RF), k-nearest neighbor (kNN), and linear regression (LR)) for AGB estimation in a subtropical region under non-stratification and stratification of forest types. The results show the following: (1) Landsat TM imagery provides more accurate AGB estimates (root mean squared error (RMSE) values in 27.7–29.3 Mg/ha) than ALOS PALSAR (RMSE values in 30.3–33.7 Mg/ha). The combination of TM and PALSAR data has similar performance for ANN and SVR, worse performance for RF and KNN, and slightly improved performance for LR. (2) Overestimation for small AGB values and underestimation for large AGB values are major problems when using the optical (e.g., Landsat) or radar (e.g., ALOS PALSAR) data. (3) LR is still an important tool for AGB modeling, especially for the AGB range of 40–120 Mg/ha. Machine learning algorithms have limited effects on improving AGB estimation overall, but ANN can improve AGB modeling when AGB values are greater than 120 Mg/ha. (4) Forest type and AGB range are important factors that influence AGB modeling performance. (5) Stratification based on forest types improved AGB estimation, especially when AGB was greater than 160 Mg/ha, using the LR approach. This research provides new insight for remote sensing-based AGB modeling for the subtropical forest ecosystem through a comprehensive analysis of different source data, modeling algorithms, and forest types. It is critical to develop an optimal AGB modeling procedure, including the collection of a sufficient number of sample plots, extraction of suitable variables and modeling algorithms, and evaluation of the AGB estimates.

Effects of large aboveground biomass loss events on the deadwood and litter mass dynamics of seasonal tropical forests in Cambodia

Effects of large aboveground biomass loss events on the deadwood and litter mass dynamics of seasonal tropical forests in Cambodia