Browsing by Author "Lewis, SL"

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    ItemOpen Access
    Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission
    (Remote Sensing of Environment, 2022-03-01) Duncanson, L; Kellner, JR; Armston, J; Dubayah, R; Minor, DM; Hancock, S; Healey, SP; Patterson, PL; Saarela, S; Marselis, S; Silva, CE; Bruening, J; Goetz, SJ; Tang, H; Hofton, M; Blair, B; Luthcke, S; Fatoyinbo, L; Abernethy, K; Alonso, A; Andersen, HE; Aplin, P; Baker, TR; Barbier, N; Bastin, JF; Biber, P; Boeckx, P; Bogaert, J; Boschetti, L; Boucher, PB; Boyd, DS; Burslem, DFRP; Calvo-Rodriguez, S; Chave, J; Chazdon, RL; Clark, DB; Clark, DA; Cohen, WB; Coomes, DA; Corona, P; Cushman, KC; Cutler, MEJ; Dalling, JW; Dalponte, M; Dash, J; de-Miguel, S; Deng, S; Ellis, PW; Erasmus, B; Fekety, PA; Fernandez-Landa, A; Ferraz, A; Fischer, R; Fisher, AG; García-Abril, A; Gobakken, T; Hacker, JM; Heurich, M; Hill, RA; Hopkinson, C; Huang, H; Hubbell, SP; Hudak, AT; Huth, A; Imbach, B; Jeffery, KJ; Katoh, M; Kearsley, E; Kenfack, D; Kljun, N; Knapp, N; Král, K; Krůček, M; Labrière, N; Lewis, SL; Longo, M; Lucas, RM; Main, R; Manzanera, JA; Martínez, RV; Mathieu, R; Memiaghe, H; Meyer, V; Mendoza, AM; Monerris, A; Montesano, P; Morsdorf, F; Næsset, E; Naidoo, L; Nilus, R; O'Brien, M; Orwig, DA; Papathanassiou, K; Parker, G; Philipson, C; Phillips, OL; Pisek, J; Poulsen, JR; Pretzsch, H; Rüdiger, C
    NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.
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    Old growth Afrotropical forests critical for maintaining forest carbon
    (Global Ecology and Biogeography, 2020-10-01) Poulsen, JR; Medjibe, VP; White, LJT; Miao, Z; Banak-Ngok, L; Beirne, C; Clark, CJ; Cuni-Sanchez, A; Disney, M; Doucet, JL; Lee, ME; Lewis, SL; Mitchard, E; Nuñez, CL; Reitsma, J; Saatchi, S; Scott, CT
    Aim: Large trees [≥ 70 cm diameter at breast height (DBH)] contribute disproportionately to aboveground carbon stock (AGC) across the tropics but may be vulnerable to changing climate and human activities. Here we determine the distribution, drivers and threats to large trees and high carbon forest. Location: Central Africa. Time period: Current. Major taxa studied: Trees. Methods: Using Gabon's new National Resource Inventory of 104 field sites, AGC was calculated from 67,466 trees from 578 species and 97 genera. Power and Michaelis–Menten models assessed the contribution of large trees to AGC. Environmental and anthropogenic drivers of AGC, large trees, and stand variables were modelled using Akaike’s information criterion (AIC) weights to calculate average regression coefficients for all p. ossible models. Results: Mean AGC for trees ≥ 10 cm DBH in Gabonese forestlands was 141.7 Mg C/ha, with averages of 166.6, 171.3 and 96.6 Mg C/ha in old growth, concession and secondary forest. High carbon forests occurred where large trees are most abundant: 31% of AGC was stored in large trees (2.3% of all stems). Human activities largely drove variation in AGC and large trees, but climate and edaphic conditions also determined stand variables (basal area, tree height, wood density, stem density). AGC and large trees increased with distance from human settlements; AGC was 40% lower in secondary than primary and concession forests and 33% higher in protected than non-managed areas. Main conclusions: AGC and large trees were negatively associated with human activities, highlighting the importance of forest management. Redefining large trees as ≥ 50 cm DBH (4.3% more stems) would account for 20% more AGC. This study demonstrates that protecting relatively undisturbed forests can be disproportionately effective in conserving carbon and suggests that including sustainable forestry in programs like reduced emissions for deforestation and forest degradation could maintain carbon dense forests in logging concessions that are a large proportion of remaining Central African forests.
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    The global abundance of tree palms
    (Global Ecology and Biogeography, 2020-09-01) Muscarella, R; Emilio, T; Phillips, OL; Lewis, SL; Slik, F; Baker, WJ; Couvreur, TLP; Eiserhardt, WL; Svenning, JC; Affum-Baffoe, K; Aiba, SI; de Almeida, EC; de Almeida, SS; de Oliveira, EA; Álvarez-Dávila, E; Alves, LF; Alvez-Valles, CM; Carvalho, FA; Guarin, FA; Andrade, A; Aragão, LEOC; Murakami, AA; Arroyo, L; Ashton, PS; Corredor, GAA; Baker, TR; de Camargo, PB; Barlow, J; Bastin, JF; Bengone, NN; Berenguer, E; Berry, N; Blanc, L; Böhning-Gaese, K; Bonal, D; Bongers, F; Bradford, M; Brambach, F; Brearley, FQ; Brewer, SW; Camargo, JLC; Campbell, DG; Castilho, CV; Castro, W; Catchpole, D; Cerón Martínez, CE; Chen, S; Chhang, P; Cho, P; Chutipong, W; Clark, C; Collins, M; Comiskey, JA; Medina, MNC; Costa, FRC; Culmsee, H; David-Higuita, H; Davidar, P; del Aguila-Pasquel, J; Derroire, G; Di Fiore, A; Van Do, T; Doucet, JL; Dourdain, A; Drake, DR; Ensslin, A; Erwin, T; Ewango, CEN; Ewers, RM; Fauset, S; Feldpausch, TR; Ferreira, J; Ferreira, LV; Fischer, M; Franklin, J; Fredriksson, GM; Gillespie, TW; Gilpin, M; Gonmadje, C; Gunatilleke, AUN; Hakeem, KR; Hall, JS; Hamer, KC; Harris, DJ; Harrison, RD; Hector, A; Hemp, A; Herault, B; Pizango, CGH; Coronado, ENH; Hubau, W; Hussain, MS; Ibrahim, FH; Imai, N; Joly, CA; Joseph, S; Anitha, K; Kartawinata, K; Kassi, J; Killeen, TJ
    Aim: Palms are an iconic, diverse and often abundant component of tropical ecosystems that provide many ecosystem services. Being monocots, tree palms are evolutionarily, morphologically and physiologically distinct from other trees, and these differences have important consequences for ecosystem services (e.g., carbon sequestration and storage) and in terms of responses to climate change. We quantified global patterns of tree palm relative abundance to help improve understanding of tropical forests and reduce uncertainty about these ecosystems under climate change. Location: Tropical and subtropical moist forests. Time period: Current. Major taxa studied: Palms (Arecaceae). Methods: We assembled a pantropical dataset of 2,548 forest plots (covering 1,191 ha) and quantified tree palm (i.e., ≥10 cm diameter at breast height) abundance relative to co-occurring non-palm trees. We compared the relative abundance of tree palms across biogeographical realms and tested for associations with palaeoclimate stability, current climate, edaphic conditions and metrics of forest structure. Results: On average, the relative abundance of tree palms was more than five times larger between Neotropical locations and other biogeographical realms. Tree palms were absent in most locations outside the Neotropics but present in >80% of Neotropical locations. The relative abundance of tree palms was more strongly associated with local conditions (e.g., higher mean annual precipitation, lower soil fertility, shallower water table and lower plot mean wood density) than metrics of long-term climate stability. Life-form diversity also influenced the patterns; palm assemblages outside the Neotropics comprise many non-tree (e.g., climbing) palms. Finally, we show that tree palms can influence estimates of above-ground biomass, but the magnitude and direction of the effect require additional work. Conclusions: Tree palms are not only quintessentially tropical, but they are also overwhelmingly Neotropical. Future work to understand the contributions of tree palms to biomass estimates and carbon cycling will be particularly crucial in Neotropical forests.