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ArticleImprovement of the forest cover-changes cartography from global forest change for critical deforestation regions in Mexico. Case of the Lacandona Region 2014-2021
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.Global Forest Change (GFC) is a global monitoring system with moderate resolution (Landsat, 30 m pixel) that allows knowing the location and magnitude of the losses or gains of global forest cover. Critical Forest Change (CFC) is a calibration system based on comprehensive photo interpretation (1:10,000 scale for change editing, and 1:5,000 scale for interpretation- confirmation of change strata), with diagnostic criteria supported by field data of the National Forest Inventory (scale 1:1, period 2014-2021). CFC reduces until 85.8% the overestimation of the forest loss of GFC in the case of the Lacandona Region (327,646 ha). The process included the analysis of data at 330 study sites and the interpretation of 1,190 frames of Spot-6 (April 28, 2014) versus Sentinel-2 (April 24, 2021) in higher resolution (10 m). The annual rate of forest loss obtained by GFC (4,526 ha.yr-1) is 1.87 times higher than the LFC rate (2,415 ha.yr-1). Through a comparative analysis between the cartography of GFC and CFC, it was possible to identify that 19.2% of the differences correspond to phenological changes (leaf fall deciduous, greenness variation, or alteration of the biomass due to eventual changes in humidity). 31.3% by Landsat spatial resolution limitations, 3.8% occurs in changes by industrial plantations, 11.6% of the differences can reduce by eliminating the GFC residuals outside the forest FAO definition (changes less than 0.5 ha), 7.4% of the differences correspond to atmospheric noise in the interpreted images, 6.8% to visual omissions and 19.9% there are no changes by interpretation. The cartographic adjustment of GFC by CFC is relatively fast (1,000 ha.hr-1 per photointerpreter-expert). Its implementation improves the spatial coherence, periodicity, and legibility of the areas of change, strengthening the relevance of both systems in local policy decisions. Cartographic results of this work are available at http://selvalacandona.ecosur.ourecosystem.com Keywords: Selva Lacandona; Critical Forest Change; Global Forest Change; Forest Monitoring; Deforestation. ID: 3624121 -
ArticleReducing risks from forest fire and disasters through a community-based forest fire brigade (MPA), a case study in Danau Sentarum National Park
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.The TNBKDS is an exotic ecosystem and has designated as one of the world biospheres reserves. The Park is also inhabited by around 6,000 people. Ecotourism, biodiversity, and cultural value attracts many domestic and international tourists. However, the park has high-risk from forest fire and flooding. During 2014-2019, forest fire in the park is recorded at the average of 206.6 Ha. The lake has also experienced in regular flooding. Reducing these risks should involve community by strengthening their capacity to protect their home. Consultations and SWOT analysis was used to map community capacity and to rank the threat. Opportunities on livelihood improvement is also identified to develop approach and strategy in reducing risks and improving their income. A GIS tool was used to monitor forest fire. A total of 10 MPA were established in 10 villages, involving 300 peoples (300 households). From series of consultations, 100% agreed that community need to involve in combating forest fire and reducing the damage from flooding. FIP-1 provides forest fire equipment, as well as series of training on forest fire, forest monitoring using GPS, alternative income activities such as bee keeping, fish processing, and women empowerment. Establishment of MPA and implementation of forest patrol for the period of 2018-Jun 2021 has directly protected a forest area from forest fire of 11,265 ha and non-forest area of 82,481 ha. Community participation is the key success of reducing risks from forest fire. Training program is also essential to support community capacity in reducing hotspots and to provide alternative income for their sustainable live within the national park. Keywords: forest fire, community, West Kalimantan, climate change ID: 3488239 -
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
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