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Implications of forest definition for quantifying disturbance regime characteristics in Mediterranean forests

XV World Forestry Congress, 2-6 May 2022









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    Progression of hyperspectral remote sensing for estimation of forest health to comply with SDGs
    XV World Forestry Congress, 2-6 May 2022
    2022
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    Forests offer many crucial services to sustain life on Earth as they produce timber, maintain hydrological status and biodiversity conservation, sequesters carbon dioxide (CO2), which play a role in mitigating climate change.Forest’s health is an important parameter to maintain in the dire face of tremendous anthropogenic and natural pressure over many parts of the world. Remote sensing technology offers an opportunity to assess forest health at a local, regional to a global scale. New advancements such as hyperspectral remote sensing (HRS) can provide improved forest health estimation and monitoring. The present study classified hyperspectral Airborne Visible Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) data into four broad classes forests, agriculture, fallow land and water bodies. We used the supervised classification method spectral angle mapper (SAM) to classify hyperspectral image based on endmembers produced. Then, we derived the greenness index, leaf pigment index, canopy water and light use efficiency index and dry or senescent carbon index parameters to monitor the health status of forest tropical of the Shoolpaneshwar wildlife sanctuary (SWS), Gujarat, India. The classification map shows that the dense forest cover in SWS is mainly found in the inner parts of SWS, while the outer parts are occupied with agriculture and fallow land. The health parameters mapsrevealed that the outside zones have low forest health, while the inner forest of SWS has good health. This research effectively uses advanced remote sensing hyperspectral data for forest health monitoring, which helps planning and sustainable forest management. Keywords: Forest health, hyperspectral, AVIRIS-NG, Remote sensing ID: 3623691
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    C.A.F.E: A multi-objective decision support system for eco-hydrological forest management that quantifies and optimizes different ecosystem services. 2022
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    Sustainable forest management is a powerful nature-based solution for climate-change adaptation and mitigation. In this sense, knowledge of the ecosystem services (ES) generated by forests is essential to plan and implement efficient management alternatives, especially when resources are threatened by climate change. Even more so in forests with low timber productivity, such as semi-arid Mediterranean ecosystems, where forest management based exclusively on timber products, which is the most easily monetizable service and therefore the most attractive for companies and individuals, is not profitable. C.A.F.E. (Carbon, Aqua, Fire & Eco-resilience) is a Multi-Objective Decision Support System for forest management that quantifies and optimizes ES derived from forest management, thus paving the way to payment for ES schemes. It is based on the combination of multiple pyro-eco-hydrological processes simulated by process-based models and multi-criteria optimization with genetic evolutionary algorithms. This tool allows managers to plan the silvicultural operations oriented towards thinning or planting necessary for multi-criteria forest management, answering the following 4 fundamental questions: How much, where, when or how do I have to act? In addition, it allows to see how climate change scenarios influence silvicultural actions and the production of goods and ES. The provided results are a list of possible silvicultural actions (Pareto front), each of one, associated with the quantification of the targeted ES and compared to the base line situation. As Pareto front, all solutions provided are equally valid and none is better than the other. To select a final solution, users must establish their priorities in terms of ES by filtering the solutions with the help of an iterative visualization interface. Keywords: Sustainable forest management, Climate change, Knowledge management, Landscape management, Innovation. ID:3623151
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    The change in forest productivity and stand-dynamics under climate change in East Asian temperate forests: A case study from South Korean forests
    XV World Forestry Congress, 2-6 May 2022
    2022
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    The velocity and impact of climate change on forest appear to be site, environment, and tree species-specific.The primary objective of this research is to assess the changes in productivity of major temperate tree species in South Korea using terrestrial inventory and satellite remote sensing data. The area covered by each tree species was further categorized into either lowland forest (LLF) or high mountain forest (HMF) and investigated. We used the repeated Korean national forest inventory (NFI) data to calculate a stand-level annual increment (SAI). We then compared the SAI, a ground-based productivity measure, to MODIS net primary productivity (NPP) as a measure of productivity based on satellite imagery. In addition, the growth index of each increment core, which eliminated the effect of tree age on radial growth, was derived as an indicator of the variation of productivity by tree species over the past four decades. Based on these steps, we understand the species- and elevation-dependent dynamics. The secondary objective is to predict the forest dynamics under climate change using the Perfect Plasticity Approximation with Simple Biogeochemistry (PPA- SiBGC) model. The PPA-SiBGC is an analytically tractable model of forest dynamics, defined in terms of parameters for individual trees, including allometry, growth, and mortality. We estimated these parameters for the major species by using NFI and increment core data. We predicted forest dynamics using the following time-series metrics: Net ecosystem exchange, aboveground biomass, belowground biomass, C, soil respiration, and relative abundance. We then focus on comparing the impact of climate change on LLF and HMF. The results of our study can be used to develop climate-smart forest management strategies to ensure that both LLF and HMF continue to be resilient and continue to provide a wide range of ecosystem services in the Eastern Asian region. Keywords: mountain forests, lowland forests, increment core, national forest inventory, MODIS NPP ID: 3486900

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