Satellite imagery as a single source of predictor variables for habitat suitability modelling: how Landsat can inform the conservation of a critically endangered lemur

1. Statistical modelling of habitat suitability is an important tool for planning conservation interventions, particularly for areas where species distribution data are expensive or hard to collect. Sometimes however the predictor variables typically used in habitat suitability modelling are themselves difficult to obtain or not meaningful at the geographical extent of the study, as is the case for the Alaotran gentle lemur Hapalemur alaotrensis, a critically endangered lemur confined to the marshes of Lake Alaotra in Madagascar.2. We developed a habitat suitability model where all predictor variables, including vegetation indices and image texture measures at different scales (as surrogates for habitat structure), were derived from Landsat7 satellite imagery. Using relatively few presence records, the maximum entropy (Maxent) approach and AUC were used to assess the performance of candidate predictor variables, for studying the effect of scale, model selection and mapping suitable habitat.3. This study demonstrated the utility of satellite imagery as a single source of predictor variables for a Maxent habitat suitability model at the landscape level, within a restricted geographical extent and with a fine grain, in a case where predictor variables typically used at the macro-scale level (e.g. climatic and topographic) were not applicable.4. In the case of H. alaotrensis, the methodology generated a habitat suitability map to inform conservation management in Lake Alaotra and a replicable protocol to allow rapid updates to habitat suitability maps in the future. The exploration of candidate predictor variables allowed the identification of scales that appear ecologically relevant for the species.5. Synthesis and applications. This study presents a cost-effective combination of maximum entropy habitat suitability modelling and satellite imagery, where all predictor variables are derived solely from Landsat7 images. With a habitat modelling method like Maxent that shows good performance with few presence samples and Landsat images now freely available, the methodology can play an important role in rapid assessments of the status of species at the landscape level in data-poor regions, when typical macro-scale environmental predictors are of little use or difficult to obtain.

Mapping land use in a large agricultural basin : a comparison between classification techniques

In order to facilitate the better management of river basin resources, the Glenelg-Hopkins region in south-east Australia required an accurate and up to date land use map. Land use has a major impact on Australia's natural resources including its soil, water, flora and fauna and plays a major role in determining basin health. Inappropriate land use and practices have contributed to extensive dryland salinity and water quality problems. Land use data is often required for environmental models and in most cases the reliability of model outputs is dependent on the spatial detail and accuracy of the land use mapping. This paper examines methods to obtain an up to date land use map and a detailed accuracy assessment using Landsat ETM+ data for a regional basin. A multi-source based approach allowed the collection of 4817 ground truth data points from the field investigation. This enabled researchers to (i) incorporate a full range of information into digital image analysis with significant improvements in accuracy and (ii) hold sufficient independent references for an accurate error assessment. Classification accuracy was significantly improved using a stratification design, in which the region is sub-divided into smaller homogenous areas as opposed to a full scene classification technique. The overall classification accuracy was 84% (KHAT= 0.833) for the stratified approach compared to 76% (KHAT= 0.743) for the full scene classification. Effective assessment, planning and management of basins are dependent on a sound knowledge of the distribution and variability of land use.

Multi-temporal land use mapping using remotely sensed techniques and the integration of a pollutant load model in a GIS

Estimation of nutrient load production based on multi-temporal remotely sensed land-use data for the Glenelg-Hopkins region in southwest Victoria, Australia, is discussed. Changes in land use were mapped using archived Landsat data and computerized classification techniques. Land-use
change is unparalleled in recent history, with 13% of the region transformed in the last decade. Total nitrogen and phosphorus loading were estimated using an export coefficient model. The analysis demonstrated a disturbing increase in nitrogen and phosphorus loadings from 1995 to 2002. Whilst such increases were suspected from past anecdotal and ad-hoc evidence, our modelling quantitatively estimated such increases and thus demonstrated the enormous potential of using remote sensing and GIS for monitoring land-use change and hence improve land-use management.

The consequences of land use change on nutrient exports: a regional scale assessment in south-west Victoria, Australia

Estimation of nutrient load production based on multi-temporal remotely sensed land use data for the Glenelg–Hopkins region in south-west Victoria, Australia, is discussed. Changes in land use were mapped using archived Landsat data and computerised classification techniques. Land use change has been rapid in recent history with 16% of the region transformed in the last 22 years. Total nitrogen and phosphorus loads were estimated using an export coefficient model. The analysis demonstrates an increase in modelled nitrogen and phosphorus loadings from 1980 to 2002. Whilst such increases were suspected from past anecdotal and ad-hoc evidence, our modelling estimated the magnitude of such increases and thus demonstrated the enormous potential of using remote sensing and GIS for monitoring regional scale environmental processes.

Investigating the distribution of acacia longifolia ssp. sophorae in south-west Victoria using satellite remote sensing and GIS

Mapping and analysis of the distribution of environmental weeds is an important component of strategic weed management. Such information is particularly important in managing 'native invaders', where invasion characteristics must be clearly understood prior to any management action being taken. This paper reports on an investigation of the current distribution of the native invader Acacia longifolia ssp. sophorae (Labill.) Court (coast wattle) in south-west Victoria, using remote sensing and Geographic Information Systems (GIS). Coast wattle was successfully mapped from Landsat ETM imagery using a supervised classification procedure, with 82%, of coast wattle shown on the map accurately depicting coast wattle on the ground. An estimated 11,448 ha were classified as supporting coast wattle, representing 12% of native vegetation in the study area. A more detailed GIS analysis in the Lower Glenelg National Park revealed coast wattle has invaded a limited number of vegetation types, and is more prevalent close to roads and within management zones associated with disturbance. The current regional extent of the species means widespread control is unlikely; hence the immediate focus should be on preventing further spread into areas where it is currently absent. Landsat imagery also proved to be a successful tool for mapping large scale coast wattle distribution, and could be used in long-term monitoring of the species.

Evidence for Megalake Chad, north-central Africa, during the late quaternary from satellite data

The existence of a very large Lake Chad during the late Quaternary, Megalake Chad, has long been questioned. A Megalake Chad would present strong evidence for climatic fluctuations of great magnitude during the Holocene in tropical Africa. In this paper we used satellite data from Landsat and Modis sensors to collect and analyse new information on landforms in a 2 000 000 km2 region of the Lake Chad Basin. We detected 2300 km of remains marking the ancient shoreline of Megalake Chad. The satellite data also indicated many Saharan rivers and relict deltas leading to the long paleoshoreline. Large dunefield flattenings were observed and interpreted as the result of wave-cut erosion by the paleolake. Similarities were noticed between the landforms observed along the paleoshoreline of Megalake Chad and that of the former Aral Sea. This finding has significant consequences for reconstructing paleohydrology and paleoenvironments through the Lake Chad basin, and continental climate change.

Integration of 3-PG into SWAT to simulate the growth of evergreen forests

SWAT cannot accurately simulate the seasonal fluctuations or the long-term trend of the Leaf Area Index (LAI) of evergreen forests. This deficiency has detrimental impacts for the prediction of interception and transpiration, two processes that have a significant influence on catchment water yield. This paper details the integration of the forest growth model 3-PG with SWAT to improve the simulation of LAI for evergreen forests. The integrated model, called SWAT/3-PG, was applied to the Woady Yaloak River Catchment in southern Australia where eucalyptus forests and pine plantations account for 30% of the total land use. SWAT/3-PG simulated the LAI of eucalypts and pines more accurately and realistically than the original version of SWAT. Forest LAI simulated by SWAT/3-PG agreed reasonably well with estimates of forest LAI derived independently from a Landsat satellite image. SWAT/3- PG has considerable value as a tool that managers can utilise to predict the impacts of land use change in catchments where evergreen forests are prevalent.

A constructivist approach to the teaching & learning of astronomy

Astronomy is one of the recurrent contemporary issues in the mass media where news related to comets, new stars, satellites, space tests, etc., frequently appear. Through this media presence students develop an interest in learning about aspects of astronomy. However, the information students gain from various sources, both inside and outside the classroom, doesn't often increase their knowledge about the most basic and common aspects of astronomy (Martinez Pena & Gil Quilez, 2001). Studies that have explored students' and teachers' understandings of astronomical concepts (Kalkan et al, 2007; Trumper, 2001) have found many alternative conceptions relating to basic astronomical processes such as day and night, the seasons, gravity and the relative distances between celestial objects.

Annual variation in the distribution of summer snowdrifts in the Kosciuszko alpine area, Australia, and its effect on the composition and structure of alpine vegetation

Australian alpine ecosystems are expected to diminish in extent as global warming intensifies. Alpine vegetation patterns are influenced by the duration of snow cover including the presence of snowdrifts in summer, but there is little quantitative information on landscape-scale relationships between vegetation patterns and the frequency of occurrence of persistent summer snowdrifts in the Australian alps. We mapped annual changes in summer snowdrifts in the Kosciuszko alpine region, Australia, from Landsat TM images and modelled the frequency of occurrence of persistent summer snowdrifts from long-term records (1954–2003) of winter snow depth. We then compared vegetation composition and structure among four classes that differed in the frequency of occurrence of persistent summer snowdrifts. We found a curvilinear relationship between annual winter snow depth and the area occupied by persistent snowdrifts in the following summer (r²=0.9756). Only 21 ha (0.42% of study area) was predicted to have supported summer snowdrifts in 80% of the past 50 years, while 440 ha supported persistent summer snow in 10% of years. Mean cover and species richness of vascular plants declined significantly, and species composition varied significantly, as the frequency of summer snow persistence increased. Cushion plants and rushes were most abundant where summer snowdrifts occurred most frequently, and shrubs, grasses and sedges were most abundant in areas that did not support snowdrifts in summer. The results demonstrate strong regional relationships between vegetation composition and structure and the frequency of occurrence of persistent summer snowdrifts. Reductions in winter snow depth due to global warming are expected to lead to substantial reductions in the extent of persistent summer snowdrifts. As a consequence, shrubs, grasses and sedges are predicted to expand at the expense of cushion plants and rushes, reducing landscape vegetation diversity. Fortunately, few vascular plant species (e.g. Ranunculus niphophilus) appear to be totally restricted to areas where summer snow occurs most frequently. The results from this study highlight potential indicator species that could be monitored to assess the effects of global warming on Australian alpine environments.

Solid state NMR characterization of lithium conducting ceramics

⁷Li solid state NMR has been used to characterize lithium aluminium titanium phosphate and lithium lanthanum titanate ceramics. Both materials have high ionic mobilities at room temperature and this is reflected in their static ⁷Li powder patterns. In the case of the phosphate based ceramic, a narrow Lorentzian peak is observed above 300 K, which narrows further with increasing temperature. The accompanying quadrupolar structure, with C_Q (quadrupolar coupling constant) ~ 40 kHz, suggests that the lithium ions are hopping rapidly between equivalent, high electric field gradient sites. The ²⁷Al and ³¹P magic angle spinning (MAS) spectra reveal an asymmetric phosphorus peak and two distinct aluminium resonances. The room temperature powder pattern of Li_0.33La_0.57TiO₃ shows a dipolar broadened peak which narrows quite suddenly at 310 K revealing quadrupolar satellites with C_Q ~ 900 Hz. A second lithium site is also observed in this material, as indicated by a further, weaker quadrupolar structure (C_Q ~ 40 kHz).

Measurement error causes scale-dependent threshold erosion of biological signals in animal movement data

Recent advances in telemetry technology have created a wealth of tracking data available for many animal species moving over spatial scales from tens of meters to tens of thousands of kilometers. Increasingly, such data sets are being used for quantitative movement analyses aimed at extracting fundamental biological signals such as optimal searching behavior and scale-dependent foraging decisions. We show here that the location error inherent in various tracking technologies reduces the ability to detect patterns of behavior within movements. Our analyses endeavored to set out a series of initial ground rules for ecologists to help ensure that sampling noise is not misinterpreted as a real biological signal. We simulated animal movement tracks using specialized random walks known as Lévy flights at three spatial scales of investigation: 100-km, 10-km, and 1-km maximum daily step lengths. The locations generated in the simulations were then blurred using known error distributions associated with commonly applied tracking methods: the Global Positioning System (GPS), Argos polar-orbiting satellites, and light-level geolocation. Deviations from the idealized Lévy flight pattern were assessed for each track after incrementing levels of location error were applied at each spatial scale, with additional assessments of the effect of error on scale-dependent movement patterns measured using fractal mean dimension and first-passage time (FPT) analyses. The accuracy of parameter estimation (Lévy μ, fractal mean D, and variance in FPT) declined precipitously at threshold errors relative to each spatial scale. At 100-km maximum daily step lengths, error standard deviations of ≥10 km seriously eroded the biological patterns evident in the simulated tracks, with analogous thresholds at the 10-km and 1-km scales (error SD ≥ 1.3 km and 0.07 km, respectively). Temporal subsampling of the simulated tracks maintained some elements of the biological signals depending on error level and spatial scale. Failure to account for large errors relative to the scale of movement can produce substantial biases in the interpretation of movement patterns. This study provides researchers with a framework for understanding the limitations of their data and identifies how temporal subsampling can help to reduce the influence of spatial error on their conclusions.

Monitoring protected areas using remote sensing technology

Due to irrational use of natural resources, human society is facing unprecedented threats. Remote sensing is one of the essential tools to determine changes in various forms of biological diversity over time. There are many methods to determine changes in protected areas, using satellite images. In this paper after introducing different change detection methods and their advantages and disadvantages, a hybrid method is used to analyse changes in forests and protected areas in a national park. Two Landsat images of Golestan National Park in Iran (taken in 1998 and 2010) were used. This hybrid approach combines Change Vector Analysis (CVA) for flagging the occurrence of changes, followed by signature extension to assign labels to changedpixels. The main objective of this paper is to propose a method for discovering and assessing environmental threats to natural treasures.

The accuracy of Fastloc-GPS locations and implications for animal tracking

Over recent years, a major breakthrough in marine animal tracking has occurred with the advent of Fastloc-GPS that provides highly accurate location data even for animals that only surface briefly such as sea turtles, marine mammals and penguins. We assessed the accuracy of Fastloc-GPS locations using fixed trials of tags in which >45 000 locations were obtained. Procedures for determining the speed of travel and heading were developed by simulating tracks and then adding Fastloc-GPS location errors. The levels of detail achievable for speed and heading estimates were illustrated by using empirical Fastloc-GPS data for a green turtle (Chelonia mydas, Linnaeus, 1758) travelling over 3000 km across the Indian Ocean. The accuracy of Fastloc-GPS locations varied as a function of the number of GPS satellites used in the location calculation. For example, when Fastloc-GPS locations were calculated using 4 GPS satellites, 50% of locations were within 36 m and 95% within 724 m of the true position. These values improved to 18 and 70 m, respectively, when 6 satellites were used. Simulations indicated that for animals travelling around 2·5 km h-1 (e.g. turtles, penguins and seals) and depending on the number of satellites used in the location calculation, robust speed and heading estimates would usually be obtained for locations only 1-6 h apart. Fastloc-GPS accuracy is several orders of magnitude better that conventional Argos tracking or light-based geolocation and consequently will allow new insights into small-scale movement patterns of marine animals.