A global seasonal surface ocean climatology of phytoplankton types based on CHEMTAX analysis of HPLC pigments

Much advancement has been made in recent years in field data assimilation, remote sensing and ecosystem modeling, yet our global view of phytoplankton biogeography beyond chlorophyll biomass is still a cursory taxonomic picture with vast areas of the open ocean requiring field validations. High performance liquid chromatography (HPLC) pigment data combined with inverse methods offer an advantage over many other phytoplankton quantification measures by way of providing an immediate perspective of the whole phytoplankton community in a sample as a function of chlorophyll biomass. Historically, such chemotaxonomic analysis has been conducted mainly at local spatial and temporal scales in the ocean. Here, we apply a widely tested inverse approach, CHEMTAX, to a global climatology of pigment observations from HPLC. This study marks the first systematic and objective global application of CHEMTAX, yielding a seasonal climatology comprised of ~1500 1°x1° global grid points of the major phytoplankton pigment types in the ocean characterizing cyanobacteria, haptophytes, chlorophytes, cryptophytes, dinoflagellates, and diatoms, with results validated against prior regional studies where possible. Key findings from this new global view of specific phytoplankton abundances from pigments are a) the large global proportion of marine haptophytes (comprising 32 ± 5% of total chlorophyll), whose biogeochemical functional roles are relatively unknown, and b) the contrasting spatial scales of complexity in global community structure that can be explained in part by regional oceanographic conditions. These publicly accessible results will guide future parameterizations of marine ecosystem models exploring the link between phytoplankton community structure and marine biogeochemical cycles.

Geospatial analysis of groundwater depth for manual well drilling suitability in the Zinder Region, Niger

Manual and low-tech well drilling techniques have potential to assist in reaching the United Nations' millennium development goal for water in sub-Saharan Africa. This study used publicly available geospatial data in a regression tree analysis to predict groundwater depth in the Zinder region of Niger to identify suitable areas for manual well drilling. Regression trees were developed and tested on a database for 3681 wells in the Zinder region. A tree with 17 terminal leaves provided a range of ground water depth estimates that were appropriate for manual drilling, though much of the tree's complexity was associated with depths that were beyond manual methods. A natural log transformation of groundwater depth was tested to see if rescaling dataset variance would result in finer distinctions for regions of shallow groundwater. The RMSE for a log-transformed tree with only 10 terminal leaves was almost half that of the untransformed 17 leaf tree for groundwater depths less than 10 m. This analysis indicated important groundwater relationships for commonly available maps of geology, soils, elevation, and enhanced vegetation index from the MODIS satellite imaging system.

Coral reef habitat maps derived from a high-spatial-resolution multi-spectral satellite image using object based image analysis

Coral reef maps at various spatial scales and extents are needed for mapping, monitoring, modelling, and management of these environments. High spatial resolution satellite imagery, pixel <10 m, integrated with field survey data and processed with various mapping approaches, can provide these maps. These approaches have been accurately applied to single reefs (10-100 km**2), covering one high spatial resolution scene from which a single thematic layer (e.g. benthic community) is mapped. This article demonstrates how a hierarchical mapping approach can be applied to coral reefs from individual reef to reef-system scales (10-1000 km**2) using object-based image classification of high spatial resolution images guided by ecological and geomorphological principles. The approach is demonstrated for three individual reefs (10-35 km**2) in Australia, Fiji, and Palau; and for three complex reef systems (300-600 km**2) one in the Solomon Islands and two in Fiji. Archived high spatial resolution images were pre-processed and mosaics were created for the reef systems. Georeferenced benthic photo transect surveys were used to acquire cover information. Field and image data were integrated using an object-based image analysis approach that resulted in a hierarchically structured classification. Objects were assigned class labels based on the dominant benthic cover type, or location-relevant ecological and geomorphological principles, or a combination thereof. This generated a hierarchical sequence of reef maps with an increasing complexity in benthic thematic information that included: 'reef', 'reef type', 'geomorphic zone', and 'benthic community'. The overall accuracy of the 'geomorphic zone' classification for each of the six study sites was 76-82% using 6-10 mapping categories. For 'benthic community' classification, the overall accuracy was 52-75% with individual reefs having 14-17 categories and reef systems 20-30 categories. We show that an object-based classification of high spatial resolution imagery, guided by field data and ecological and geomorphological principles, can produce consistent, accurate benthic maps at four hierarchical spatial scales for coral reefs of various sizes and complexities.