Geographic information system-based decision support for soil conservation planning in Tajikistan

Soil erosion on sloping agricultural land poses a serious problem for the environment, as well as for production. In areas with highly erodible soils, such as those in loess zones, application of soil and water conservation measures is crucial to sustain agricultural yields and to prevent or reduce land degradation. The present study, carried out in Faizabad, Tajikistan, was designed to evaluate the potential of local conservation measures on cropland using a spatial modelling approach to provide decision-making support for the planning of spatially explicit sustainable land use. A sampling design to support comparative analysis between well-conserved units and other field units was established in order to estimate factors that determine water erosion, according to the Revised Universal Soil Loss Equation (RUSLE). Such factor-based approaches allow ready application using a geographic information system (GIS) and facilitate straightforward scenario modelling in areas with limited data resources. The study showed first that assessment of erosion and conservation in an area with inhomogeneous vegetation cover requires the integration of plot-based cover. Plot-based vegetation cover can be effectively derived from high-resolution satellite imagery, providing a useful basis for plot-wise conservation planning. Furthermore, thorough field assessments showed that 25.7% of current total cropland is covered by conservation measures (terracing, agroforestry and perennial herbaceous fodder). Assessment of the effectiveness of these local measures, combined with the RUSLE calculations, revealed that current average soil loss could be reduced through low-cost measures such as contouring (by 11%), fodder plants (by 16%), and drainage ditches (by 53%). More expensive measures such as terracing and agroforestry can reduce erosion by as much as 63% (for agroforestry) and 93% (for agroforestry combined with terracing). Indeed, scenario runs for different levels of tolerable erosion rates showed that more cost-intensive and technologically advanced measures would lead to greater reduction of soil loss. However, given economic conditions in Tajikistan, it seems advisable to support the spread of low-cost and labourextensive measures.

Modelización espacial de huevos de Thrips (Thysanoptera: Frankliniella occidentalis) en tomate de cáscara por medio de técnicas geoestadísticas

El tomate de cáscara (Physalis ixocarpa Brot.) es un cultivo alimenticio de gran importancia económica en México. Sin embargo, es afectado por diversas plagas y enfermedades tales como los Thrips (Thysanoptera: Frankliniella occidentalis) y el virus de la marchitez manchada del tomate (TSWV) que llegan a causar hasta un 80% de pérdidas. El objetivo del presente trabajo fue modelizar la distribución espacial de huevos de Thrips mediante técnicas geoestadísticas y obtener, en consecuencia, mapas de incidencia por medio del Kriging. Se georreferenciaron 121 puntos de muestreo en cada una de las parcelas comerciales de los municipios de Luvianos, Jocotitlán e Ixtlahuaca, a través del método de transectos en tres etapas fenológicas del cultivo. Se contabilizó el número de huevos de Thrips en cada punto de muestreo. Los resultados mostraron que las poblaciones de huevos deThrips presentan una distribución agregada, identificándose varios centros de conglomeración a través de los mapas obtenidos. Los semivariogramas obtenidos de la distribución espacial se ajustaron principalmente a los modelos gaussianos y esféricos. La distribución de huevos de Thrips se presentó en centros de agregación dentro de las parcelas estudiadas, lo cual permitirá establecer estrategias y medidas de control o mitigación en términos de sitios específicos de infestación de huevos de Thrips.

Fish communities as indicators of biological conditions of rivers: methods for reference conditions

Fish communities are a key element in fluvial ecosystems Their position in the top of the food chain and their sensitivity to a whole range of impacts make them a clear objective for ecosystem conservation and a sound indicator of biological integrity. The UE Water Framework Directive includes fish community composition, abundance and structure as relevant elements for the evaluation os biological condition. Several approaches have been proposed for the evaluation of the condition of fish communities, from the bio-indicator concept to the IBI (Index of biotic integrity) proposals. However, the complexity of fish communities and their ecological responses make this evaluation difficult, and we must avoid both oversimplified and extreme analytical procedures. In this work we present a new proposal to define reference conditions in fish communities, discussing them from an ecological viewpoint. This method is a synthetic approach called SYNTHETIC OPEN METHODOLOGICAL FRAMEWORK (SOMF) that has been applied to the rivers of Navarra. As a result, it is recommended the integration of all the available information from spatial, modelling, historical and expert sources, providing the better approach to fish reference conditions, keeping the highest level of information and meeting the legal requirements of the WFD.

Spatial data mining for enhanced soil map modelling

The principle of using induction rules based on spatial environmental data to model a soil map has previously been demonstrated Whilst the general pattern of classes of large spatial extent and those with close association with geology were delineated small classes and the detailed spatial pattern of the map were less well rendered Here we examine several strategies to improve the quality of the soil map models generated by rule induction Terrain attributes that are better suited to landscape description at a resolution of 250 m are introduced as predictors of soil type A map sampling strategy is developed Classification error is reduced by using boosting rather than cross validation to improve the model Further the benefit of incorporating the local spatial context for each environmental variable into the rule induction is examined The best model was achieved by sampling in proportion to the spatial extent of the mapped classes boosting the decision trees and using spatial contextual information extracted from the environmental variables.

Multilevel Modelling with Spatial Effects

In multilevel modelling, interest in modeling the nested structure of hierarchical data has been accompanied by increasing attention to different forms of spatial interactions across different levels of the hierarchy. Neglecting such interactions is likely to create problems of inference, which typically assumes independence. In this paper we review approaches to multilevel modelling with spatial effects, and attempt to connect the two literatures, discussing the advantages and limitations of various approaches.

Spatial econometric issues for bio-economic and land-use modelling

We survey the literature on spatial bio-economic and land-use modelling and assess its thematic development. Unobserved site-specific heterogeneity is a feature of almost all the surveyed works, and this feature, it seems, has stimulated significant methodological innovation. In an attempt to improve the suitability with which the prototype incorporates heterogeneity, we consider modelling alternatives and extensions. We discuss solutions and conjecture others.

Spatial econometric issues for bio-economic and land-use modelling

We survey the literature on spatial bio-economic and land-use modelling and assess its thematic development. Unobserved site-specific heterogeneity is a feature of almost all the surveyed works, and this feature, it seems, has stimulated significant methodological innovation. In an attempt to improve the suitability with which the prototype incorporates heterogeneity, we consider modelling alternatives and extensions. We discuss solutions and conjecture others.

The benefits of a compute cluster approach to high spatial resolution biodiversity richness modelling: projecting the impact of climate change on Mediterranean flora

High spatial resolution environmental data gives us a better understanding of the environmental factors affecting plant distributions at fine spatial scales. However, large environmental datasets dramatically increase compute times and output species model size stimulating the need for an alternative computing solution. Cluster computing offers such a solution, by allowing both multiple plant species Environmental Niche Models (ENMs) and individual tiles of high spatial resolution models to be computed concurrently on the same compute cluster. We apply our methodology to a case study of 4,209 species of Mediterranean flora (around 17% of species believed present in the biome). We demonstrate a 16 times speed-up of ENM computation time when 16 CPUs were used on the compute cluster. Our custom Java ‘Merge’ and ‘Downsize’ programs reduce ENM output files sizes by 94%. The median 0.98 test AUC score of species ENMs is aided by various species occurrence data filtering techniques. Finally, by calculating the percentage change of individual grid cell values, we map the projected percentages of plant species vulnerable to climate change in the Mediterranean region between 1950–2000 and 2020.

Modelling of urban sensible heat flux at multiple spatial scales : a demonstration using airborne hyperspectral imagery of Shanghai and a temperature–emissivity separation approach

Urbanization related alterations to the surface energy balance impact urban warming (‘heat islands’), the growth of the boundary layer, and many other biophysical processes. Traditionally, in situ heat flux measures have been used to quantify such processes, but these typically represent only a small local-scale area within the heterogeneous urban environment. For this reason, remote sensing approaches are very attractive for elucidating more spatially representative information. Here we use hyperspectral imagery from a new airborne sensor, the Operative Modular Imaging Spectrometer (OMIS), along with a survey map and meteorological data, to derive the land cover information and surface parameters required to map spatial variations in turbulent sensible heat flux (QH). The results from two spatially-explicit flux retrieval methods which use contrasting approaches and, to a large degree, different input data are compared for a central urban area of Shanghai, China: (1) the Local-scale Urban Meteorological Parameterization Scheme (LUMPS) and (2) an Aerodynamic Resistance Method (ARM). Sensible heat fluxes are determined at the full 6 m spatial resolution of the OMIS sensor, and at lower resolutions via pixel aggregation and spatial averaging. At the 6 m spatial resolution, the sensible heat flux of rooftop dominated pixels exceeds that of roads, water and vegetated areas, with values peaking at ∼ 350 W m− 2, whilst the storage heat flux is greatest for road dominated pixels (peaking at around 420 W m− 2). We investigate the use of both OMIS-derived land surface temperatures made using a Temperature–Emissivity Separation (TES) approach, and land surface temperatures estimated from air temperature measures. Sensible heat flux differences from the two approaches over the entire 2 × 2 km study area are less than 30 W m− 2, suggesting that methods employing either strategy maybe practica1 when operated using low spatial resolution (e.g. 1 km) data. Due to the differing methodologies, direct comparisons between results obtained with the LUMPS and ARM methods are most sensibly made at reduced spatial scales. At 30 m spatial resolution, both approaches produce similar results, with the smallest difference being less than 15 W m− 2 in mean QH averaged over the entire study area. This is encouraging given the differing architecture and data requirements of the LUMPS and ARM methods. Furthermore, in terms of mean study QH, the results obtained by averaging the original 6 m spatial resolution LUMPS-derived QH values to 30 and 90 m spatial resolution are within ∼ 5 W m− 2 of those derived from averaging the original surface parameter maps prior to input into LUMPS, suggesting that that use of much lower spatial resolution spaceborne imagery data, for example from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is likely to be a practical solution for heat flux determination in urban areas.