Linking riparian vegetation spatial structure in Australian tropical savannas to ecosystem health indicators: semi-variogram analysis of high spatial resolution satellite imagery

Government agencies responsible for riparian environments are assessing the combined utility of field survey and remote sensing for mapping and monitoring indicators of riparian zone health. The objective of this work was to determine if the structural attributes of savanna riparian zones in northern Australia can be detected from commercially available remotely sensed image data. Two QuickBird images and coincident field data covering sections of the Daly River and the South Alligator River - Barramundie Creek in the Northern Territory were used. Semi-variograms were calculated to determine the characteristic spatial scales of riparian zone features, both vegetative and landform. Interpretation of semi-variograms showed that structural dimensions of riparian environments could be detected and estimated from the QuickBird image data. The results also show that selecting the correct spatial resolution and spectral bands is essential to maximize the accuracy of mapping spatial characteristics of savanna riparian features. The distribution of foliage projective cover of riparian vegetation affected spectral reflectance variations in individual spectral bands differently. Pan-sharpened image data enabled small-scale information extraction (< 6 m) on riparian zone structural parameters. The semi-variogram analysis results provide the basis for an inversion approach using high spatial resolution satellite image data to map indicators of savanna riparian zone health.

Population monitoring for kangaroo management

In wildlife management, the program of monitoring will depend on the management objective. If the objective is damage mitigation, then ideally it is damage that should be monitored. Alternatively, population size (N) can be used as a surrogate for damage, but the relationship between N and damage obviously needs to be known. If the management objective is a sustainable harvest, then the system of monitoring will depend on the harvesting strategy. In general, the harvest strategy in all states has been to offer a quota that is a constant proportion of population size. This strategy has a number of advantages over alternative strategies, including a low risk of over- or underharvest in a stochastic environment, simplicity, robustness to bias in population estimates and allowing harvest policy to be proactive rather than reactive. However, the strategy requires an estimate of absolute population size that needs to be made regularly for a fluctuating population. Trends in population size and in various harvest statistics, while of interest, are secondary. This explains the large research effort in further developing accurate estimation methods for kangaroo populations. Direct monitoring on a large scale is costly. Aerial surveys are conducted annually at best, and precision of population estimates declines with the area over which estimates are made. Management at a fine scale (temporal or spatial) therefore requires other monitoring tools. Indirect monitoring through harvest statistics and habitat models, that include rainfall or a greenness index from satellite imagery, may prove useful.

Regional patterns of agricultural land use and deforestation in Colombia

An expanding human population and associated demands for goods and services continues to exert an increasing pressure on ecological systems. Although the rate of expansion of agricultural lands has slowed since 1960, rapid deforestation still occurs in many tropical countries, including Colombia. However, the location and extent of deforestation and associated ecological impacts within tropical countries is often not well known. The primary aim of this study was to obtain an understanding of the spatial patterns of forest conversion for agricultural land uses in Colombia. We modeled native forest conversion in Colombia at regional and national-levels using logistic regression and classification trees. We investigated the impact of ignoring the regional variability of model parameters, and identified biophysical and socioeconomic factors that best explain the current spatial pattern and inter-regional variation in forest cover. We validated our predictions for the Amazon region using MODIS satellite imagery. The regional-level classification tree that accounted for regional heterogeneity had the greatest discrimination ability. Factors related to accessibility (distance to roads and towns) were related to the presence of forest cover, although this relationship varied regionally. In order to identify areas with a high risk of deforestation, we used predictions from the best model, refined by areas with rural population growth rates of > 2%. We ranked forest ecosystem types in terms of levels of threat of conversion. Our results provide useful inputs to planning for biodiversity conservation in Colombia, by identifying areas and ecosystem types that are vulnerable to deforestation. Several of the predicted deforestation hotspots coincide with areas that are outstanding in terms of biodiversity value.

Unplanned land clearing of Colombian rainforests: Spreading like disease?

Deforestation often occurs as temporal waves and in localized fronts termed 'deforestation hotspots' driven by economic pulses and population pressure. Of particular concern for conservation planning are 'biodiversity hotspots' where high concentrations of endemic species undergo rapid loss and fragmentation of habitat. We investigate the deforestation process in Caqueta, a biodiversity hotspot and major colonization front of the Colombian Amazon using multi-temporal satellite imagery of the periods 1989-1996-1999-2002. The probabilities of deforestation and regeneration were modeled against soil fertility, accessibility and neighborhood terms, using logistic regression analysis. Deforestation and regeneration patterns and rates were highly variable across the colonization front. The regional average annual deforestation rate was 2.6%, but varied locally between -1.8% (regeneration) and 5.3%, with maximum rates in landscapes with 40-60% forest cover and highest edge densities, showing an analogous pattern to the spread of disease. Soil fertility and forest and secondary vegetation neighbors showed positive and significant relationships with the probability of deforestation. For forest regeneration, soil fertility had a significant negative effect while the other parameters were marginally significant. The logistic regression models across all periods showed a high level of discrimination power for both deforestation and forest regeneration, with ROC values > 0.80. We document the effect of policies and institutional changes on the land clearing process, such as the failed peace process between government and guerillas in 1999-2002, which redirected the spread of deforestation and increased forest regeneration. The implications for conservation in biologically rich areas, such as Caqueta are discussed. (c) 2005 Elsevier B.V All rights reserved.

Mud, algae or seagrass: An assessment of remote sensing techniques for mapping inter-tidal seagrass cover and composition in turbid tropical environments

Large areas of tropical sub- and inter-tidal seagrass beds occur in highly turbid environments and cannot be mapped through the water column. The purpose of this project was to determine if and how airborne and satellite imaging systems could be used to map inter-tidal seagrass properties along the wet-tropics coast in north Queensland, Australia. The work aimed to: (1) identify the minimum level of seagrass foliage cover that could be detected from airborne and satellite imagery; and (2) define the minimum detectable differences in seagrass foliage cover in exposed intertidal seagrass beds. High resolution spectral-reflectance data (2040 bands, 350 – 2500nm) were collected over 40cm diameter plots from 240 sites on Magnetic Island, Pallarenda Beach and Green Island in North Queensland at spring low tides in April 2006. The seagrass species sampled were: Thalassia hemprechii, Halophila ovalis, Halodule uninerivs; Syringodium isoetifolium, Cymodocea serrulata, and Cymodoea rotundata. Digital photos were captured for each plot and used to derive estimates of seagrass species cover, epiphytic growth, micro- and macro-algal cover, and substrate colour. Sediment samples were also collected and analysed to measure the concentration of Chlorophyll-a associated with benthic micro-algae. The field reflectance spectra were analysed in combination with their corresponding seagrass species foliage cover levels to establish the minimum foliage projective cover required for each seagrass to be significantly different from bare substrate and substrate with algal cover. This analysis was repeated with reflectance spectra resampled to the bandpass functions of Quickbird, Ikonos, SPOT 5 and Landsat 7 ETM. Preliminary results indicate that conservative minimum detectable seagrass cover levels across most the species sampled were between 30%- 35% on dark substrates. Further analysis of these results will be conducted to determine their separability and satellite images and to assess the effects epiphytes and algal cover.

A composite DOP approach to excluding bottom reflectance in mapping water parameters of shallow coastal zones from TM imagery

In this paper we proposed a composite depth of penetration (DOP) approach to excluding bottom reflectance in mapping water quality parameters from Landsat thematic mapper (TM) data in the shallow coastal zone of Moreton Bay, Queensland, Australia. Three DOPs were calculated from TM1, TM2 and TM3, in conjunction with bathymetric data, at an accuracy ranging from +/-5% to +/-23%. These depths were used to segment the image into four DOP zones. Sixteen in situ water samples were collected concurrently with the recording of the satellite image. These samples were used to establish regression models for total suspended sediment (TSS) concentration and Secchi depth with respect to a particular DOP zone. Containing identical bands and their transformations for both parameters, the models are linear for TSS concentration, logarithmic for Secchi depth. Based on these models, TSS concentration and Secchi depth were mapped from the satellite image in respective DOP zones. Their mapped patterns are consistent with the in situ observed ones. Spatially, overestimation and underestimation of the parameters are restricted to localised areas but related to the absolute value of the parameters. The mapping was accomplished more accurately using multiple DOP zones than using a single zone in shallower areas. The composite DOP approach enables the mapping to be extended to areas as shallow as <3 m. (C) 2004 Elsevier Inc. All rights reserved.

Monitoring the condition of savanna riparian zones in Northern Australia using high spatial resolution imagery and a field based rapid assessment method

Government agencies responsible for riparian environments are assessing the combined utility of field survey and remote sensing for mapping and monitoring indicators of riparian zone condition. The objective of this work was to compare the Tropical Rapid Appraisal of Riparian Condition (TRARC) method to a satellite image based approach. TRARC was developed for rapid assessment of the environmental condition of savanna riparian zones. The comparison assessed mapping accuracy, representativeness of TRARC assessment, cost-effectiveness, and suitability for multi-temporal analysis. Two multi-spectral QuickBird images captured in 2004 and 2005 and coincident field data covering sections of the Daly River in the Northern Territory, Australia were used in this work. Both field and image data were processed to map riparian health indicators (RHIs) including percentage canopy cover, organic litter, canopy continuity, stream bank stability, and extent of tree clearing. Spectral vegetation indices, image segmentation and supervised classification were used to produce RHI maps. QuickBird image data were used to examine if the spatial distribution of TRARC transects provided a representative sample of ground based RHI measurements. Results showed that TRARC transects were required to cover at least 3% of the study area to obtain a representative sample. The mapping accuracy and costs of the image based approach were compared to those of the ground based TRARC approach. Results proved that TRARC was more cost-effective at smaller scales (1-100km), while image based assessment becomes more feasible at regional scales (100-1000km). Finally, the ability to use both the image and field based approaches for multi-temporal analysis of RHIs was assessed. Change detection analysis demonstrated that image data can provide detailed information on gradual change, while the TRARC method was only able to identify more gross scale changes. In conclusion, results from both methods were considered to complement each other if used at appropriate spatial scales.