Above ground biomass and tree species richness estimation with airborne lidar in tropical Ghana forests |
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| Institution: | 1. Impacts of Agriculture, Forests and Ecosystem Services Division, Euro-Mediterranean Center on Climate Change (IAFES-CMCC), via Pacinotti 5, Viterbo 01100, Italy;2. Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, Viterbo 01100, Italy;3. Consiglio per la ricerca in agricoltura e l''analisi dell''economia agraria, Forestry Research Centre (CRA-SEL), Via Santa Margherita 80, I-52100 Arezzo, Italy;4. Department of Plant Sciences, University of Oxford, South Parks Road, Oxford OX1 3RB, UK;5. Santa Catarina State University (UDESC), Av. Luiz de Camoes, 2090, Lages, Santa Catarina 88520-000, Brazil;6. Department of Geography, University of Hawai''i at Mānoa, 422 Saunders Hall, 2424 Maile Way, Honolulu, HI 96822, USA;1. Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, NMBU, P.O. Box 5003, NO-1432 Ås, Norway;2. Department of Forest Resource Management, Swedish University of Agricultural Sciences, SLU, Skogsmarksgränd, SE-901 83 Umeå, Sweden;1. Dipartimento di Bioscienze e Territorio, University of Molise, Contrada Fonte Lappone, Pesche, IS 86090, Italy;2. Northern Research Station, U.S. Forest Service, Saint Paul, Minnesota 55108, USA;3. Department of Agricultural, Food and Forestry Systems, University of Florence, Florence 50145, Italy |
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| Abstract: | Estimates of forest aboveground biomass are fundamental for carbon monitoring and accounting; delivering information at very high spatial resolution is especially valuable for local management, conservation and selective logging purposes. In tropical areas, hosting large biomass and biodiversity resources which are often threatened by unsustainable anthropogenic pressures, frequent forest resources monitoring is needed. Lidar is a powerful tool to estimate aboveground biomass at fine resolution; however its application in tropical forests has been limited, with high variability in the accuracy of results. Lidar pulses scan the forest vertical profile, and can provide structure information which is also linked to biodiversity. In the last decade the remote sensing of biodiversity has received great attention, but few studies focused on the use of lidar for assessing tree species richness in tropical forests.This research aims at estimating aboveground biomass and tree species richness using discrete return airborne lidar in Ghana forests. We tested an advanced statistical technique, Multivariate Adaptive Regression Splines (MARS), which does not require assumptions on data distribution or on the relationships between variables, being suitable for studying ecological variables.We compared the MARS regression results with those obtained by multilinear regression and found that both algorithms were effective, but MARS provided higher accuracy either for biomass (R2 = 0.72) and species richness (R2 = 0.64). We also noted strong correlation between biodiversity and biomass field values. Even if the forest areas under analysis are limited in extent and represent peculiar ecosystems, the preliminary indications produced by our study suggest that instrument such as lidar, specifically useful for pinpointing forest structure, can also be exploited as a support for tree species richness assessment. |
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| Keywords: | Biomass Carbon monitoring Lidar Species richness Tropical forests West Africa |
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