Use of a leaf chlorophyll content index to improve the prediction of above-ground biomass and productivity.
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ABSTRACT:
SUBMITTER: Liu C
PROVIDER: S-EPMC6330949 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The effects of leaf litter on moisture content and fungal decay development in above-ground wood specimens were assessed. Untreated southern pine specimens were exposed with or without leaf litter contact. Two types of leaf litter were evaluated; aged (decomposed) and young (early stages of decomposition). The moisture content of specimens was monitored, and specimens were periodically removed for visual evaluation of decay development. In addition, amplicon-based sequencing analysis of specimens and associated leaf litter was conducted at two time points. Contact with either type of leaf litter resulted in consistently higher moisture contents than those not in contact with leaf litter. Visually, evident decay developed most rapidly in specimens in contact with the aged leaf litter. Analysis of amplicon-based sequencing revealed that leaf litter contributes a significant amount of the available wood decay fungal community with similar communities found in the litter exposed wood and litter itself, but dissimilar community profiles from unexposed wood. Dominant species and guild composition shifted over time, beginning initially with more leaf saprophytes (ascomycetes) and over time shifting to more wood rotting fungi (basidiomycetes). These results highlight the importance of the contributions of leaf litter to fungal colonization and subsequent decay hazard for above-ground wood.
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Project description:Above-ground biomass (AGB) is an important indicator for effectively assessing crop growth and yield and, in addition, is an important ecological indicator for assessing the efficiency with which crops use light and store carbon in ecosystems. However, most existing methods using optical remote sensing to estimate AGB cannot observe structures below the maize canopy, which may lead to poor estimation accuracy. This paper proposes to use the stem-leaf separation strategy integrated with unmanned aerial vehicle LiDAR and multispectral image data to estimate the AGB in maize. First, the correlation matrix was used to screen optimal the LiDAR structural parameters (LSPs) and the spectral vegetation indices (SVIs). According to the screened indicators, the SVIs and the LSPs were subjected to multivariable linear regression (MLR) with the above-ground leaf biomass (AGLB) and above-ground stem biomass (AGSB), respectively. At the same time, all SVIs derived from multispectral data and all LSPs derived from LiDAR data were subjected to partial least squares regression (PLSR) with the AGLB and AGSB, respectively. Finally, the AGB was computed by adding the AGLB and the AGSB, and each was estimated by using the MLR and the PLSR methods, respectively. The results indicate a strong correlation between the estimated and field-observed AGB using the MLR method (R 2 = 0.82, RMSE = 79.80 g/m2, NRMSE = 11.12%) and the PLSR method (R 2 = 0.86, RMSE = 72.28 g/m2, NRMSE = 10.07%). The results indicate that PLSR more accurately estimates AGB than MLR, with R 2 increasing by 0.04, root mean square error (RMSE) decreasing by 7.52 g/m2, and normalized root mean square error (NRMSE) decreasing by 1.05%. In addition, the AGB is more accurately estimated by combining LiDAR with multispectral data than LiDAR and multispectral data alone, with R 2 increasing by 0.13 and 0.30, respectively, RMSE decreasing by 22.89 and 54.92 g/m2, respectively, and NRMSE decreasing by 4.46% and 7.65%, respectively. This study improves the prediction accuracy of AGB and provides a new guideline for monitoring based on the fusion of multispectral and LiDAR data.
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Project description:Carbon emissions resulting from deforestation and forest degradation are poorly known at local, national and global scales. In part, this lack of knowledge results from uncertain above-ground biomass estimates. It is generally assumed that using more sophisticated methods of estimating above-ground biomass, which make use of remote sensing, will improve accuracy. We examine this assumption by calculating, and then comparing, above-ground biomass area density (AGBD) estimates from studies with differing levels of methodological sophistication. We consider estimates based on information from nine different studies at the scale of Africa, Mozambique and a 1160 km(2) study area within Mozambique. The true AGBD is not known for these scales and so accuracy cannot be determined. Instead we consider the overall precision of estimates by grouping different studies. Since an the accuracy of an estimate cannot exceed its precision, this approach provides an upper limit on the overall accuracy of the group. This reveals poor precision at all scales, even between studies that are based on conceptually similar approaches. Mean AGBD estimates for Africa vary from 19.9 to 44.3 Mg ha(-1), for Mozambique from 12.7 to 68.3 Mg ha(-1), and for the 1160 km(2) study area estimates range from 35.6 to 102.4 Mg ha(-1). The original uncertainty estimates for each study, when available, are generally small in comparison with the differences between mean biomass estimates of different studies. We find that increasing methodological sophistication does not appear to result in improved precision of AGBD estimates, and moreover, inadequate estimates of uncertainty obscure any improvements in accuracy. Therefore, despite the clear advantages of remote sensing, there is a need to improve remotely sensed AGBD estimates if they are to provide accurate information on above-ground biomass. In particular, more robust and comprehensive uncertainty estimates are needed.
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