ABSTRACT
The largest actively cycling terrestrial carbon pool, soil, has been disturbed during latest centuries by human actions through reduction of woody land cover. Soil organic carbon (SOC) content can reliably be estimated in laboratory conditions, but more cost-efficient and mobile techniques are needed for large-scale monitoring of SOC e.g. in remote areas. We demonstrate the capability of a mobile hyperspectral camera operating in the visible-near infrared wavelength range for practical estimation of soil organic carbon (SOC) and nitrogen content, to support efficient monitoring of soil properties. The 191 soil samples were collected in Taita Taveta County, Kenya representing an altitudinal gradient comprising five typical land use types: agroforestry, cropland, forest, shrubland and sisal estate. The soil samples were imaged using a Specim IQ hyperspectral camera under controlled laboratory conditions, and their carbon and nitrogen content was determined with a combustion analyzer. We use machine learning for estimating SOC and N content based on the spectral images, studying also automatic selection of informative wavelengths and quantification of prediction uncertainty. Five alternative methods were all found to perform well with a cross-validated R2 of approximately 0.8 and an RMSE of one percentage point, demonstrating feasibility of the proposed imaging setup and computational pipeline.
ABSTRACT
AIM: Conservation and land-use planning require accurate maps of patterns in species composition and an understanding of the factors that control them. Substantial doubt exists, however, about the existence and determinants of large-area floristic divisions in Amazonia. Here we ask whether Amazonian forests are partitioned into broad-scale floristic units on the basis of geological formations and their edaphic properties. LOCATION: Western and central Amazonia. METHODS: We used Landsat imagery and Shuttle Radar Topography Mission (SRTM) digital elevation data to identify a possible floristic and geological discontinuity of over 300 km in northern Peru. We then used plant inventories and soil sampling to document changes in species composition and soil properties across this boundary. Data were obtained from 138 sites distributed along more than 450 km of road and river. On the basis of our findings, we used broad-scale Landsat and SRTM mosaics to identify similar patterns across western and central Amazonia. RESULTS: The discontinuity identified in Landsat and SRTM data corresponded to a 15-fold change in soil cation concentrations and an almost total change in plant species composition. This discontinuity appears to be caused by the widespread removal of cation-poor surface sediments by river incision to expose cation-rich sediments beneath. Examination of broad-scale Landsat and SRTM mosaics indicated that equivalent processes have generated a north-south discontinuity of over 1500 km in western Brazil. Due to similarities with our study area, we suggest that this discontinuity represents a chemical and ecological limit between western and central Amazonia. MAIN CONCLUSIONS: Our findings suggest that Amazonian forests are partitioned into large-area units on the basis of geological formations and their edaphic properties. The evolution of these units through geological time may provide a general mechanism for biotic diversification in Amazonia. These compositional units, moreover, may correspond to broad-scale functional units. The existence of large-area compositional and functional units would suggest that protected-area, carbon sequestration, and other land-use strategies in Amazonia be implemented on a region-by-region basis. The methods described here can be used to map these patterns, and thus enable effective conservation and management of Amazonian forests.
