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ArticleThe use of Normalized Difference Vegetation Index (NDVI) to assess urban forests dynamics in West Africa: A case study of Mbao Classified Forest, Dakar (Senegal)
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.Mbao Classified Forest is the largest urban forest in Dakar. It covers an area of 720 hectares and is the most important green lung of the city. This forest plays a key role in terms of carbon storage and sequestration, air pollution removal, and more generally in ecosystem services provision. Hence it is urgent to monitor the dynamic of this forest over the past twenty years (1998-2018) because a lot of infrastructures including a water pumping station and a highway were established inside during this period. These installations make it subject to encroachments and the risk of depletion that could compromise its existence. The aim of this this paper is to assess urban forest dynamics using artificial intelligence and vegetation indices. To achieve this goal the first step is to perform a forest inventory. We opted for a sampling rate of 0.5%. The area of a plot in the i-Tree Eco inventory is 391 m2 with a radius of 11.16 m, which resulted in a total number of 90 plots. The variables measured for each tree are D.B.H, total height, crown width. The allometric equations were used to compute the above-ground biomass. The NDVI of every plot was computed from Landsat datasets followed by the development of a linear regression model with NDVI as the independent variable and biomass as the dependent variable. Landsat imagery enables the NDVI computation of each plot during the twenty past years and using the regression model, the biomass was determined over this period. Our results provide a sound basis to advocate the safeguarding of Mbao Classified Forest. Keywords: Urban forest, biomass, NDVI, inventory. ID: 3621874 -
ArticleThe 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
2022Also available in:
No results found.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 -
ArticleTowards the development of a strategy for sustainable commercialization of non-timber forest products in Kenya: A situational analysis
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.Non-Timber Forest Products (NTFPs) play a significant role in the livelihoods of Kenyans. This paper reports the key strengths that could be optimized, opportunities available, weaknesses that need to be mitigated, and threats that require recognition to have a strategy for the sustainable commercialization of NTFPs in Kenya. This study was funded by The Restoration Initiative (TRI) project being implemented by FAO and other partners. It involved consultations with 50 institutions and a review of relevant publications, reports, policies, legislation, and strategies. The key interventions in the sub-sector include research and development, resource assessment and mapping, value chain analyses, capacity building, value addition, piloting plantation production, and policy reviews for a limited number of products such as gums and resins, honey, aloes, and mushrooms. The major stakeholders are collectors, community groups, traders, National government agencies, County Governments, private sector actors, development partners, and civil society organizations. Key barriers to the commercialization of NTFPs include deforestation, traditional production, and harvesting technologies, inadequate bulking facilities, insufficient value addition, weak market linkages, and information systems as well as weak policy and institutional frameworks. It is concluded that sustainable commercialization of these products in the country requires a strategy that involves revision/domestication of laws and policies, public-private partnerships, research, innovation, value addition, technology development and transfer, capacity building, synergies and complementarities. Keywords: Non-timber forest products, situational analysis, strategy, Kenya ID: 3485349
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