Tingbao Xu

Fractals, fractal dimensions and landscapes — a review

Abstract Mandelbrots fractal geometry is a revolution in topological space theory and, for the first time, provides the possibility of simulating and describing landscapes precisely by using a mathematical model. Fractal analysis appears to capture some “new” information that traditional parameters do not contain. A landscape should be (or is at most) statistically self-similar or statistically self-affine if it possesses a fractal nature. Mandelbrots fractional Brownian motion (fBm) is the most useful mathematical model for simulating landscape surfaces. The fractal dimensions for different landscapes and calculated by different methods are difficult to compare. The limited size of the regions surveyed and the spatial resolution of the digital elevation models (DEMs) limit the precision and stability of the computed fractal dimension. Interpolation artifacts of DEMs and anisotropy create additional difficulties in the computation of fractal dimensions. Fractal dimensions appear to be spatially variable over landscapes. The region-dependent spatial variation of the dimension has more practical significance than the scale-dependent spatial variation. However, it is very difficult to use the fractal dimension as a distributed geomorphic parameter with high “spatial resolution”. The application of fractals to landscape analysis is a developing and immature field and much of the theoretical rigour of fractal geometry has not yet been exploited. The physical significance of landscape fractal characteristics remains to be explained. Research in geographical information theory and fractal theory needs to be strengthened in order to improve the application of fractal geometry to the geosciences.

New developments and applications in the ANUCLIM spatial climatic and bioclimatic modelling package

ANUCLIM (Xu and Hutchinson, 2011) is a unique software package used to support the spatial modelling and mapping of environmental and natural resources. It has been extensively employed for scientific research, teaching and policy making across study areas at various spatial scales. The package enables users to readily interrogate estimated values, in point and grid form, of monthly, seasonal and annual mean climate variables from supplied elevation dependent monthly mean climate surfaces and an underlying digital elevation model (DEM). The climate surfaces have been derived by the ANUSPLIN package (Hutchinson, 2004) and support interrogation at sub-kilometre scale. A key strength of the ANUCLIM package is its ability to generate bioclimatic profiles from known species locations to predict and map species distributions, in current, projected future and past climates. It can also generate a comprehensive set of climate parameters and growth indices for modelling growth of crops and plants. The package currently has four programs, MTHCLIM, BIOCLIM, BIOMAP and GROCLIM. MTHCLIM is used to obtain estimates of monthly mean climate variables from supplied climate surfaces at specified points or grids. BIOCLIM, in conjunction with BIOMAP, is a bioclimatic prediction system based on the bioclimatic envelope method devised by Nix (1986). GROCLIM is used to generate plant growth indices based on a simplified model of plant growth response to light, thermal and water regimes (Nix, 1981). The latest version of ANUCLIM, Version 6.1, incorporates substantial upgrades. In particular, the package now allows each of its four component programs to systematically incorporate the impacts of projected climate change. These projected climate changes can be provided either as simple constants, or more commonly, as grids of broad scale changes as obtained from outputs of General Circulation Models (GCMs) under various emission scenarios. For Australia, such grids can be obtained from the OzClim website of CSIRO (2007). This enables the systematic investigation of the impacts of projected climate change on socio-environmental systems.

Convergent specialization - the sharing of pollinators by sympatric genera of sexually deceptive orchids

Summary 1. Pollinator sharing can offer powerful insights into the floral traits associated with the evolution of a pollination system and the consequences of floral differences for pollinator behaviour. Here, we investigate the first known case of pollinator sharing between two sexually deceptive plant genera. Floral manipulations were used to test the importance of floral traits for pollinator behaviour and pollination efficiency. We also explored the ecological differences enabling species co-occurrence. 2. Drakaea livida and Caladenia pectinata (Orchidaceae) exhibit dramatic differences in floral display and the insectiform appearance of the labellum, yet both are pollinated by sexually attracted males of the thynnine wasp Zaspilothynnis nigripes. Because of the prevalence of cryptic species in some genera of thynnine wasps, we confirmed pollinator sharing by a mark–recapture study and sequencing of the mtDNA CO1 region. 3. Floral dissections revealed that semiochemicals used to attract the pollinator are released from the labellum in D. livida and sepaline clubs in C. pectinata. Drakaea livida was more efficient at converting pollinator attraction into potential pollen deposition leading to higher fruit set. Floral manipulations showed that pollinator contact with the labellum increases when it is the point of semiochemical release. However, sexual attraction to the labellum remained infrequent in C. pectinata in all experimental treatments. 4. While their distribution and climatic range show extensive overlap, the differences in edaphic requirements of the two orchid species suggest that they rarely co-occur. Therefore, the potential cost of sharing the same pollinator species is not realized. 5. Synthesis. This case of pollinator sharing confirms that morphological traits do not place a strong constraint on the evolution of sexual deception. However, interspecific differences in floral traits have important consequences for converting attraction into pollination, suggesting that selection can act to increase efficiency at multiple steps of the pollination process. This system provides a novel opportunity to elucidate the chemical, visual and morphological adaptations underpinning the evolution of sexual mimicry.

Assessing spatial likelihood of flooding hazard using naïve Bayes and GIS: a case study in Bowen Basin, Australia

AbstractnFlooding hazard evaluation is the basis of flooding risk assessment which has significances to natural environment, human life and social economy. This study develops a spatial framework integrating naïve Bayes (NB) and geographic information system (GIS) to assess flooding hazard at regional scale. The methodology was demonstrated in the Bowen Basin in Australia as a case study. The inputs into the framework are five indices: elevation, slope, soil water retention, drainage proximity and density. They were derived from spatial data processed in ArcGIS. NB as a simplified and efficient type of Bayesian methods was used, with the assistance of remotely sensed flood inundation extent in the sampling process, to infer flooding probability on a cell-by-cell basis over the study area. A likelihood-based flooding hazard map was output from the GIS-based framework. The results reveal elevation and slope have more significant impacts on evaluation than other input indices. Area of high likelihood of flooding hazard is mainly located in the west and the southwest where there is a high water channel density, and along the water channels in the east of the study area. High likelihood of flooding hazard covers 45xa0% of the total area, medium likelihood accounts for about 12xa0%, low and very low likelihood represents 19 and 24xa0%, respectively. The results provide baseline information to identify and assess flooding hazard when making adaptation strategies and implementing mitigation measures in future. The framework and methodology developed in the study offer an integrated approach in evaluation of flooding hazard with spatial distributions and indicative uncertainties. It can also be applied to other hazard assessments.

An integrated assessment of the impact of precipitation and groundwater on vegetation growth in arid and semiarid areas

Increased demand for water resources together with the influence of climate change has degraded water conditions which support vegetation in many parts of the world, especially in arid and semiarid areas. This study develops an integrated framework to assess the impact of precipitation and groundwater on vegetation growth in the Xiliao River Plain of northern China. The integrated framework systematically combines remote sensing technology with water flow modeling in the vadose zone and field data analysis. The vegetation growth is quantitatively evaluated with the remote sensing data by the normalized difference vegetation index (NDVI) and the simulated plant water uptake rates. The correlations among precipitation, groundwater depth and NDVI are investigated using Pearson correlation equations. The results provide insights for understanding interactions between precipitation and groundwater and their contributions to vegetation growth. Strong correlations between groundwater depth, plant water uptake and NDVI are found in parts of the study area during a ten-year drought period. The numerical modeling results indicate that there is an increased correlation between the groundwater depth and vegetation growth and that groundwater significantly contributes to sustaining effective soil moisture for vegetation growth during the long drought period. Therefore, a decreasing groundwater table might pose a great threat to the survival of vegetation during a long drought period.

Improved Urban Flooding Mapping from Remote Sensing Images Using Generalized Regression Neural Network-Based Super-Resolution Algorithm

Urban flooding is a serious natural hazard to many cities all over the world, which has dramatic impacts on the urban environment and human life. Urban flooding mapping has practical significance for the prevention and management of urban flood disasters. Remote sensing images with high temporal resolutions are widely used for urban flooding mapping, but have a limitation of relatively low spatial resolutions. In this study, a new method based on a generalized regression neural network (GRNN) is proposed to achieve improved accuracy in super-resolution mapping of urban flooding (SMUF) from remote sensing images. The GRNN-SMUF algorithm was proposed and then assessed using Landsat 5 and Landsat 8 images of Brisbane city in Australia and Wuhan city in China. Compared to three traditional methods, GRNN-SMUF mapped urban flooding more accurately according to both visual and quantitative assessments. The results of this study will improve the accuracy of urban flooding mapping using easily-available remote sensing images with medium-low spatial resolutions and will be propitious to the prevention and management of urban flood disasters.

Could native Scots pines (Pinus sylvestris) still persist in northern England and southern Scotland

Background: In the British Isles, Scots pine (Pinus sylvestris) is only thought to be native in the Scottish Highlands. However, there has been speculation that locally native specimens persist outside that region. Aims: This study addressed the question: is it bioclimatically plausible that locally native Scots pines could still persist in southern Scotland and northern England? Methods: The software package BIOCLIM, which has proved a useful tool for identifying possible locations of small populations and new species, was used to model current locations of Scots pine with climate surfaces. Based on this analysis, predictive maps were produced to identify where else in Scotland and northern England Scots pine might occur. Data were masked with soil types on which Scots pines naturally grow in Scotland to identify key areas where extant trees may still persist. Results: Results indicated that it is bioclimatically plausible that locally native Scots pines could persist in southern Scotland and northern England. However, further research is needed to confirm the natural origins of living Scots pines at particular locations. Conclusions: We propose investigations into the native status of Scots pine within the areas identified. If native Scots pines are verified outside the Scottish Highlands, this has significant implications for ecology and conservation.

Integrating Entropy-Based Naïve Bayes and GIS for Spatial Evaluation of Flood Hazard

Regional flood risk caused by intensive rainfall under extreme climate conditions has increasingly attracted global attention. Mapping and evaluation of flood hazard are vital parts in flood risk assessment. This study develops an integrated framework for estimating spatial likelihood of flood hazard by coupling weighted naïve Bayes (WNB), geographic information system, and remote sensing. The north part of Fitzroy River Basin in Queensland, Australia, was selected as a case study site. The environmental indices, including extreme rainfall, evapotranspiration, net-water index, soil water retention, elevation, slope, drainage proximity, and density, were generated from spatial data representing climate, soil, vegetation, hydrology, and topography. These indices were weighted using the statistics-based entropy method. The weighted indices were input into the WNB-based model to delineate a regional flood risk map that indicates the likelihood of flood occurrence. The resultant map was validated by the maximum inundation extent extracted from moderate resolution imaging spectroradiometer (MODIS) imagery. The evaluation results, including mapping and evaluation of the distribution of flood hazard, are helpful in guiding flood inundation disaster responses for the region. The novel approach presented consists of weighted grid data, image-based sampling and validation, cell-by-cell probability inferring and spatial mapping. It is superior to an existing spatial naive Bayes (NB) method for regional flood hazard assessment. It can also be extended to other likelihood-related environmental hazard studies.

Integration of Bayesian regulation back-propagation neural network and particle swarm optimization for enhancing sub-pixel mapping of flood inundation in river basins

ABSTRACT Sub-pixel mapping of flood inundation (SMFI) is one of the hotspots in remote sensing and relevant research and application fields. In this study, a novel method based on the integration of Bayesian regulation back-propagation neural network (BRBP) and particle swarm optimization (PSO), so-called IBRBPPSO, is proposed for SMFI in river basins. The IBRBPPSO–SMFI algorithm was developed and evaluated using Landsat images from the Changjiang river basin in China and the Murray-Darling basin in Australia. Compared with traditional SMFI methods, IBRBPPSO–SMFI consistently achieves the most accurate SMFI results in terms of visual and quantitative evaluations. IBRBPPSO–SMFI is superior to PSO–SMFI with not only an improved accuracy, but also an accelerated convergence speed of the algorithm. IBRBPPSO–SMFI reduces the uncertainty in mapping inundation in river basins by improving the accuracy of SMFI. The result of this study will also enrich the SMFI methodology, and thereby benefit the environmental studies of river basins.

Prediction of salt transport in different soil textures under drip irrigation in an arid zone using the SWAGMAN Destiny model

In recent years, Xinjiang Oasis has faced a major challenge of increasing risk of secondary salinization caused by drip irrigation under plastic mulch. Predicting the salt balance is therefore essential for understanding how to sustain the use of salinized land in this arid area. This research validated the SWAGMAN (Salt, Water And Groundwater MANagement) Destiny model to simulate and forecast the movement of salt in different soil textures based on field experiments. The results were verified with extensive field work in Shihutan, Xinjiang, China. They show that soil salinity decreases in the upper layers and increases in the bottom layers of the investigated soil profile. The desalinization rate in sand, which shows an overall steady trend throughout the soil profile, is generally higher than that in loam and clay. The depth of 60cm is critical for loam and clay; soil salinity decreases above it but increases below it. Model sensitivity analysis reveals the variation of soil salinity is independent of the initial electrical conductivity setting of SWAGMAN Destiny simulations. This study indicates that numerical modelling is a useful approach for efficiently estimating the salt balance under drip irrigation. The result provides a scientific basis for making adaptive strategies to manage salinised farmlands in arid zones.