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.

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.

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.