Presentation on theme: "Estimating Anthropogenic Influence in Tropical Forests Using Charcoal Introduction Jessica Del Greco Advisors: Crystal H. McMichael, Earth System Research."— Presentation transcript:
Estimating Anthropogenic Influence in Tropical Forests Using Charcoal Introduction Jessica Del Greco Advisors: Crystal H. McMichael, Earth System Research Center, University of New Hampshire Michael Palace, Earth System Research Center, University of New Hampshire Methods +N+P +Ca+TOC Hypotheses Results Figure 1:Satellite imagery shows the location of the samples in Santarem. Summary and Conclusions Hyperspectral image data can detect vegetation canopy chemistry differences associated with soil nutrients and chemistry. The site types that differed significantly in total amounts of charcoal are logged and burnt and undisturbed. A lack of fit model indicated that spectral bands could not accurately predict the site type, probably due to the small sample size. However, when differentiating site types using only three bands in the shortwave infrared, the model was significant These spectral bands were in areas indicating canopy water amount or hydration state. Hyperspectral satellite image bands 137, 142, and 216 accurately estimated the charcoal amounts in undisturbed plots across all depths. These bands were also indicators of canopy hydration state. Charcoal was more frequent in burned and logged + burned forests at all depths analyzed. These results suggest that paleoecological and archaeological reconstructions are not feasible in areas that have recently been burned or logged + burned. Undisturbed forests or those that have been selectively logged are more useful for these types of surveys. Our next step is to tie field based measurements of forest species and structure with charcoal and remote sensing image data. Table 2: A multiple logistic regression was run to differentiate site types using spectral bands. Lack of fit model is not significant. References/Acknowledgements (1)Malhi, Y. et al. Exploring the likelihood and mechanism of a climate-change-induced dieback of the Amazon rainforest. Proceedings of the National Academy of Sciences 106, 20610-20615 (2009). (2)McMichael, C. et al. Sparse pre-Columbian human habitation in western Amazonia. Science 336, 1429-1431 (2012). Soil sampling was designed and conducted by Luiz Aragao (Exeter/INPE), Jos Barlow (Lancaster), Erika Berenguer (Lancaster), Joice Ferreira (Embrapa), and Toby Gardner (Cambridge). Any publications resulting from this work will be in full collaboration with these contributors. Table 1: A multiple linear regression was run to estimate total charcoal amount in undisturbed plots using spectral bands. Results accurately estimate charcoal amounts. Amazonian forests comprise a total area of 4.8 million km 2, and our study focused on the region of Santarem in eastern Amazonia (Figure 1). Santarem is the oldest site of human occupation in Amazonia, where research has found pottery dating from 10,000-8000 years ago. Fire is not natural in the Amazonian landscape, and most fire ignitions are a result of human activity. Fires leave fragments of charcoal in the soils below, and are commonly used to reconstruct ancient human activity in Amazonia. But, how might modern land-use influence these paleoecological and archaeological reconstructions? I examined the charcoal distribution from different forest types in the Santarem region based on modern human activity in those areas to determine how modern land use may affect paleoecological reconstructions. I also paired the charcoal data with hyperspectral remote sensing data to differentiate land-use types. Focusing on the undisturbed forests, hyperspectral data was used to estimate charcoal amounts in undisturbed plots. H1: Charcoal will be more frequent in logged + burnt and burnt forests than in the logged and undisturbed forests. H2: Hyperspectral satellite imagery bands will effectively differentiate undisturbed plots from other forest types. H3: Hyperspectral satellite imagery will be able to accurately estimate the charcoal amounts in undisturbed plots due to different aspects of vegetation growing on soil with differing amounts of charcoal. Forest type and land use classifications were made by a combination of 30 years of remote sensing imagery (Landsat) and field surveys. Soils were collected with a hand auger in increments of 10 cm to a total depth of 30 cm in randomly selected locations within undisturbed, logged, burned, and logged + burned forests. Charcoal was extracted from 50 grams of soil per sample using standard floatation and sieving techniques for fragments >500 um, and analyzed using a LW scientific stereoscope. The surface area of the charcoal within each sample was measured using a Moticam camera. Hyperspectral remote sensing image data was extracted from data collected by the Hyperion NASA experimental satellite platform for each individual sample site. Image data had a 30 m resolution and 144 useable spectral bands ranging from 350 to 2500 nm. Site types were compared for the amount of total charcoal using an ANOVA and t-test. A multiple logistic regression was run to differentiate site types using spectral bands, while a multiple linear regression was used to estimate charcoal amount in undisturbed plots using spectral bands. Whole Model Test Model -LogLikelilhoodDFChiSquareProb>ChiSq Difference11.432131922.864260.0065 Full8.664783 Reduced20.096914 Rsquare (U)0.5689 Observations(or Sum Wgts)15 Summary of Fit Adjusted R-square0.999 Root Mean Square Error0.0007 P-Value0.0005 Figure 1: A t-test was used to compare the means of the total amount of charcoal between sites.