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DocumentBiomass estimation in mangrove forests: a comparison of allometric models incorporating species and structural information
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.Improved estimates of aboveground biomass are required to improve our understanding of the productivity of mangrove forests to support the long-term conservation of these fragile ecosystems which are under threat from many natural and anthropogenic pressures. To understand how individual species affects biomass estimates in mangrove forests, five species-specific and four genus-specific allometric models were developed. Independent tree inventory data were collected from 140 sample plots to compare the aboveground biomass (AGB) among the species-specific models and seven existing frequently used pan-tropical and Sundarbans-specific generic models. The effect of individual tree species was also evaluated using model parameters for wood densities (from individual trees to the whole Sundarbans) and tree heights (individual, plot average and plot top height). All nine species-specific models explained a high percentage of the variance in tree AGB (R2 = 0.97 to 0.99) with the diameter at breast height (DBH) and total height (H). At the individual tree level, the generic allometric models overestimated AGB from 22% to 167% compared to the species-specific models. At the plot level, mean AGB varied from 111.36 Mg ha-1 to 299.48 Mg ha-1, where AGB significantly differed in all generic models compared to the species-specific models (p < 0.05). Using measured species wood density (WD) in the allometric model showed 4.5% to 9.7% less biomass than WD from a published database and other sources. When using plot top height and plot average height rather than measured individual tree height, the AGB was overestimated by 19.5 % and underestimated by 8.3% (p < 0.05). The study demonstrates that species-specific allometric models and individual tree measurements benefit biomass estimation in mangrove forests. Tree level measurement from the inventory plots, if available, should be included in allometric models to improve the accuracy of forest biomass estimates, particularly when upscaling individual trees up to the ecosystem level. Keywords: Climate change, Monitoring and data collection, Sustainable forest management ID: 3621710 -
DocumentManaging taxonomic and functional diversity is the key to sustain aboveground biomass and soil microbial diversity: A synthesis from long-term forest restoration of southern China
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.Exploring the biodiversity-ecosystem functioning relationship is one of the central goals of ecological research. Restoration is essential for supporting key ecosystem functions such as aboveground biomass production and managing soil microbial diversity. However, the relative importance of functional versus taxonomic diversity in explaining aboveground biomass and soil microbial diversity during restoration is poorly understood. Here, we used a trait-based approach to test for the importance of multiple plant diversity attributes in regulating aboveground biomass and soil microbial diversity in four 30- years-old restored subtropical forests in southern China. High-throughput Illumina sequencing was applied for detecting fungal and bacterial diversity. We show that both taxonomic and functional diversities are significant and positive regulators of aboveground biomass; however, functional diversity (FD) was more important than taxonomic diversity (TD) in controlling aboveground biomass. FD had the strongest direct effect on aboveground biomass compared with TD, soil properties, and community weighted mean (CWM) traits. Our results further indicate that leaf and root morphological traits and traits related to the nutrient content in plant tissues showed acquisitive resource use strategy which influenced aboveground biomass. In contrast to aboveground biomass, taxonomic diversity explained more of the soil microbial diversity than the FD and soil properties. Prediction of fungal richness was better than that of bacterial richness. In addition, root traits explained more variation of soil microbes than the leaf traits. Our results suggest that both TD and FD play a role in shaping aboveground biomass and soil microbial diversity; but FD is more important in supporting aboveground biomass while TD for belowground microbial diversity. These results imply that enhancing TD and FD is important to restoring and managing degraded forest landscapes. Key words: Biodiversity-Ecosystem functions; soil microbial diversity, taxonomic diversity, functional diversity, forest restoration ID: 3486373 -
ArticleSatellite remote sensing-based forest resources assessment methods for effective management and sustainable development of forests by generation of information on forests and trees outside forest cover
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.Satellite based remote sensing methods have proved to be an effective and scientifically proven method for managing and conserving forest data and resources at periodic time intervals. The forest resources monitoring methods provide useful data to forest managers for sustainable forest management at different scale and forest management units. Over the years the scientific management of forest have been a subject globally discussed incorporating the role of environmentalist, conservationist and communities associated with the forest. It has been an unhidden fact that forests have suffered tremendous pressure in developing countries on the pretext of development. It is through effective monitoring and communication of forest information and knowledge that the concerned provincial governments are forced to take remedial measures for protecting the forests. Apart from the government owned forests, termed as Recorded Forest Areas(RFA) in India, Trees outside forests(TOF) are well acknowledged as an important component of forest resources. The ToF, which basically exist as block, linear and scattered plantations on earth are captured using LISS-III sensor of Indian Remote Sensing Satellite. For the national level scale mapping, all patches of area 1hectare and above are considered for estimation. For mapping of ToF patches of size between 0.1-1hectare, high resolution data from LISSIV sensor(5.8metres resolution) is analyzed. It has been now a well-established fact that trees outside RFAs, although in small proportion, contribute significantly to forest conservation and meeting the demand of people towards minor forest produce, firewood etc. The exercise on forest change detection using a hybrid method, is effective in identification of significant forest change. The assessment of forests and ToFs using satellite data and advance image processing tools may be helpful in effective management and long term sustainability of forests in developing countries. Keywords: [Recorded Forest Area, Trees Outside Forest, National Forest Inventory, FSI, Neural Network, Machine Learning] ID: 3622277
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