Spatially-explicit Data Research Articles

Generating plausible crop distribution maps for Sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach

Large gaps exist in our knowledge of the current geographic distribution and spatial patterns of performance of crops, and these gaps are unlikely to be filled. In addition, even the spatial scale of many sub-national statistical reporting units remains too coarse to capture patterns of spatial heterogeneity in crop production and performance that are likely important from a policy and investment planning perspective. To fill these spatial data gaps we have developed and applied a meso-scale model for the spatial disaggregation of crop production. Using a cross-entropy approach, our model makes plausible pixel-scale assessments of the spatial distribution of crop production within geopolitical units (e.g. countries or sub-national provinces and districts). The pixel-scale allocations are performed through the compilation and judicious fusion of relevant spatially-explicit data, including: production statistics, land use data, satellite imagery, biophysical crop “suitability” assessments, population density, and distance to urban centers, as wells as any prior knowledge about the spatial distribution of individual crops. The development, application and validation of a prior version of the model in Brazil strongly suggested that our spatial allocation approach shows considerable promise. This paper describes efforts to generate crop distribution maps for Sub-Saharan Africa for the year 2000 using this approach. Apart from the empirical challenge of applying the approach across many countries, the application includes three significant model improvements: (1) the ability to cope with production data sources that provided different degrees of spatial disaggregation for different crops within a single country; (2) the inclusion of a digital map of irrigation intensity as a new input layer; and (3) increased disaggregation of rainfed production systems. Applying the modified spatial allocation model we generated 5 min (approximately 10 km) resolution grid maps for the following 20 major crops across Sub-Saharan Africa: barley, dry beans, cassava, cocoa, coffee, cotton, cow peas, groundnuts, maize, millet, oil palm, plantain, potato, rice, sorghum, soybeans, sugar cane, sweet potato, wheat, and yam. The approach provides plausible results but also highlights the need for much more reliable input data for the region, especially with regard to sub-national production statistics and satellite-based estimates of cropland extent and intensity.

Land Use Change with Spatially Explicit Data: A Dynamic Approach

Most of the economic literature that uses spatially-explicit data to estimate the determinants of land-use change is limited to static models and cross-sectional data sets. Recent attempts to move to a more dynamic analysis include using panel data sets and survival analysis. In this study, we use a discrete choice dynamic model of land-use where the agent’s choices are regarded as the solution to a dynamic optimization problem. The irreversibility of some decisions, expectations about future prices, and forward-looking behavior of the land operator can all be accounted for. Our results show that a model specification that incorporates some of the complexities of the decision process improves upon results found in the existing literature. First, prediction accuracy of land use change is superior to any of the existing models. Second, we demonstrate that models that do not account for transactions costs tend to overestimate the effects of changes in transportation costs.

Integrating plantation health surveillance and wood resource inventory systems using remote sensing

Summary Commercial softwood growers in Australia are keen to improve the efficiency and precision of resource inventory underpinning their timber supply commitments. At the same time, they also need to implement forest health strategies which contribute to their environmental management systems and certification process. For example, the Australian Forestry Standard requires forest managers to identify, assess and prioritise any potential damage agents that may impact on forest ecosystem health and vitality. These health programs, however, are often run parallel with, and independently of, resource inventory programs. While most large growers maintain a health surveillance program, their capacity to quantify the impact of damaging agents on stand productivity and wood volume is often limited. Quantification of productivity losses due to biotic and abiotic agents would significantly improve decisions associated with resource scheduling and allocation of resources for pest control and stand amelioration. This paper discusses how remote sensing technologies can provide spatially-explicit data that permit the integration of plantation inventory and health assessments. The emerging diversity of sensor capabilities on both satellite and airborne platforms enables the development of hierarchical monitoring programs that can be customised for individual regions. For example, the coarse-scale sensor MODIS can provide very cheap coverage suitable for frequent temporal condition monitoring (thus identifying areas requiring more detailed attention in a timely manner), whereas the new generation of high-resolution sensors are facilitating a shift from manually mapped stand polygons (e.g. those from aerial sketchmapping and aerial photographic interpretation — API) to pixel and object-based digital analysis techniques suitable for both crown and stand-level inventory and canopy health assessment on a continuous, broad-scale basis. The application of these new technologies and associated spatial analyses permits the integration of plantation inventory and health assessment, thus providing forest managers with a holistic and cost-effective approach to timber production.

Inferring the dynamics of a spatial epidemic from time-series data

Spatial interactions are key determinants in the dynamics of many epidemiological and ecological systems; therefore it is important to use spatio-temporal models to estimate essential parameters. However, spatially-explicit data sets are rarely available; moreover, fitting spatially-explicit models to such data can be technically demanding and computationally intensive. Thus non-spatial models are often used to estimate parameters from temporal data. We introduce a method for fitting models to temporal data in order to estimate parameters which characterise spatial epidemics. The method uses semi-spatial models and pair approximation to take explicit account of spatial clustering of disease without requiring spatial data. The approach is demonstrated for data from experiments with plant populations invaded by a common soilborne fungus, Rhizoctonia solani. Model inferences concerning the number of sources of disease and primary and secondary infections are tested against independent measures from spatio-temporal data. The applicability of the method to a wide range of host–pathogen systems is discussed.

Streambed landscapes: evidence that stream invertebrates respond to the type and spatial arrangement of patches.

The availability and spatial arrangement of habitat patches are known to strongly influence fauna in terrestrial ecosystems. The importance of patch arrangement is not well-studied within running-water systems where flow-induced movements of patches and of fauna could decouple habitat characteristics and faunal habitat preferences. Using small, stream-dwelling invertebrates, we asked if fauna in such systems can distinguish among patch types and if patch arrangement at their `landscape scale' (i.e., within a streambed across which they move and forage) can be linked to faunal abundance. We quantified the spatial distribution of sand and leaf patches at multiple sites on a streambed at regular intervals over a 1 $$\frac{1}{2}$$ yr period, estimated faunal abundance in the two patch types, and experimentally determined if faunal colonization varied among leaf patches that were similar structurally but differed in their potential microbial food resources. We show that despite their small size and limited swimming abilities, these stream invertebrates did respond to patch type, that specific characteristics of an individual patch influenced faunal colonization, and that the spatial arrangement of patches on the streambed was linked to field abundances. Larval chironomids and adult copepods were more abundant in leaves than in sand and preferentially colonized leaf patches made with rapidly decomposing leaves that harbored higher microbial (bacteria and fungi) abundances over leaf patches with more refractory leaves and lower microbial abundances. Further, statistical models that included spatially-explicit data on patch arrangement (e.g., patch contagion, distance between patches) explained significantly more variation in faunal abundance, than models that included only nonspatial information (e.g., date, time since last flood). Despite the fact that these fauna live in a highly dynamic environment with variable flow rates during the year, unstable patch configurations, and seasonal changes in total abundance, our findings suggest a need for aquatic ecologists to test the hypothesis that small-scale landscape attributes within streams (e.g., leaf patch aggregation) may be important to faunal dynamics. If patch aggregation has negative consequences for stream biota, streambed `landscapes' may be fundamentally different from many terrestrial landscapes due to the inherent connectivity provided by the water and the over-riding importance of patch edges. Regardless of these differences, our findings suggest that the spatial configuration of patches in a landscape may have consequences for fauna even in highly dynamic systems, in which patches move and fauna periodically experience high levels of passive dispersal.

Assessing Land-Use Impacts on Natural Resources

/ Much information is available on changes that occur in natural resources from both spatially-explicit data on environmental conditions and models of the interactions of these conditions and resources with human activities. The strategy for assessing land-use impacts on natural resources developed in this paper provides a framework for using relevant data and models to address questions of how management practices can promote both use and protection of resources. This assessment strategy integrates spatially explicit environmental data using geographic information systems (GIS) with computer models that simulate changes in land cover in response to land-use impacts. The computer models also simulate susceptibility of species to changes in habitat suitability and landscape patterns. The approach is applied to management of limestone barrens on the Oak Ridge Reservation in East Tennessee. Potential limestone barrens habitats are identified by overlaying appropriate soils, geology, slope, and land-use/land-cover conditions. Their validity is tested against known sites containing rare species that occur in these habitats. The location of habitats at risk in the aftermath of human activities is determined by using an available area model that identifies the size and proximity of sites that particular types of species can no longer use as habitat. The resulting risk map can be used in land management planning. The approach uses readily available in situ and remotely sensed data and is applicable to a wide range of locations and land-use scenarios. This approach can be refined based on needs identified by land managers and on the sensitivity of the results to the resolution of available resource information.KEY WORDS: Land management; Assessment; Habitat characterization; Limestone barrens; Ecological modeling; Geographic information systems