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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 -
ArticleEvaluating policy coherence: A case study of peatland forests on the Kampar Peninsula landscape, Indonesia
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.Conflicting policies relating to the management of multi-sectoral, multi-level and multi-actor forest uses often result in ineffective policy implementation. Methods for assessing policy coherence, however, are limited and often require an extensive evidence base which is not always available. In Indonesia, this has often led to conflicts between government agencies and other forest stakeholders. Improved methods for assessing policy coherence could assist governments and other stakeholders to navigate policy complexity and to avoid the potentially high costs of policies that are antagonistic to one another. We propose an audit of policy coherence at the landscape scale as a way of addressing this problem. We test this idea on the Kampar Peninsula, a peat landscape in Pelalawan district, Riau Province, Indonesia. To aid our audit assessment, we overlaid radar and Landsat images to depict delineations of peat protection and cultivation zones according to different legislation. Our audit revealed incoherent mapping of peat protection zones on the Kampar Peninsula, which has led to ineffective implementation of policies. We then propose three alternative protection and cultivation scenarios to that proposed by the government. Our results show that any of these alternative scenarios would provide a policy that is not only more coherent, but that also would result in more effective policy implementation. This policy audit method should have wide potential application for auditing best practice and policy effectiveness in complex landscapes across the globe and should have immediate application in helping to resolve the current issues on the Kampar Peninsular. Keywords: policy coherence; performance auditing; landscape approach, sustainable peatland management ID: 3471212 -
DocumentRelationship between forest fragmentation patterns and deforestation: the case of the Brazilian Amazon
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.Deforestation in the Brazilian Amazon is the result of social, economic, and political pressures and its rates swing accordingly. Deforestation can lead to forest fragmentation, which may mask other negative impacts. Forest fragmentation classes resulting from a Morphological Spatial Pattern Analysis (MSPA) help to establish the spatial distribution of a fragmented landscape. However, the behavior of these classes and their association with deforestation has been little studied. To address this issue, we proposed the analyses of the diversity of fragmentation classes as an indicator of forest fragmentation trajectory over time. We used Shannon's diversity index and MSPA on land-use changes and vegetation cover data to identify the evolution of fragmented forest classes for the period 1985 - 2018. The diversity of the classes was obtained for each year using TraMineR. This value was compared with the cumulated deforestation rate from 1988 to 2018. A correlation analysis was carried out to establish the relationship between diversity of fragmentation classes and deforestation. During the studied period, all but one class of fragmentation increased. Diversity increased over the years with a mean of 0.41 ± 0.07 (range 0.27 to 0.50), even during periods of reduced deforestation. The high correlation between cumulated deforestation and diversity (R^2 = 0.98), indicated the impact on the fragmentation patterns. Specific actions are needed to reduce forest fragmentation beyond those to curb Amazon deforestation. Keywords: Landscape management, Monitoring and data collection, Research ID: 3480574
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