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Center pivot irrigation systems are used to enhance crop production in many countries around the world. Establishing the location and extent of such fields provides information that assists in describing a range of agricultural metrics, including crop identification, yield forecasts, monitoring of irrigation requirements and crop water use, as well as supporting national and regional auditing, licensing and compliance efforts. However, detailed information on the number, extent and changing dynamics of agricultural fields is often lacking in many countries: nowhere more so than in developing regions. To address this lack, we performed a national scale accounting of center pivot fields in Saudi Arabia, using a three year multi-temporal analysis of Landsat-8 satellite data. A geographic object-based image analysis approach was developed based on five 50 × 50 km sub-areas extracted from Landsat data for the year 2015, and applied to delineate individual center pivot fields at a national scale for 2013, 2014 and 2015. The extent of fields was determined via a map of the annual maximum Normalized Difference Vegetation Index (NDVI), while a 15 m spatial resolution map of annual panchromatic band minimums was used to produce an edge detection layer to delineate individual adjoining fields. Amongst a range of classification parameters that were included in the object-based mapping approach, shape information, such as the center pivot field length, length:width ratio, and elliptic fit, were identified as critical parameters. Applying the rule-set that was developed from the five 50 × 50 km sub-regions to the national scale resulted in the identification of 36,052 (11,103 km2), 38,114 (11,902 km2), and 37,254 (11,555 km2) individual fields in 2013, 2014, and 2015, respectively. Approximately 94% of these fields were correctly detected, while their individually measured area was mapped with >95% combined accuracy for fields >0.225 km2 when evaluated against manually delineated fields. Smaller center pivot fields, and specifically those adjoining neighbouring fields, had lower area mapping accuracies (>91% in 75% of cases). The object-based approach allowed a national scale and multi-temporal assessment of center pivot field delineation and mapping, affording new insights into agricultural practice and providing a methodological basis for examining the impact of water management and related policy initiatives, amongst many other potential applications. Apart from filling a clear knowledge gap in Saudi Arabia, the approach has the potential to be expanded elsewhere: particularly to similar locations within the Middle East and North Africa.

This research was funded by the Department of Agriculture and Water Resources, Australian Government as part of its Rural R&D for Profit Program and Horticulture Innovation Australia Ltd., grant number ST15002 “Multi-Scale Monitoring Tools for Managing Australian Tree Crops – Industry Meets Innovation”. We would like to acknowledge the support of the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), under grant number CE140100049. We also acknowledge the farmers at the Welcome Creek and 1019 Moore Park Road macadamia sites in Bundaberg for allowing us access to collect field and UAV data. Prof Matthew F. McCabe and Dr Kasper Johansen were supported by KAUST.

The Great Barrier Reef (GBR) is of immense biological, cultural and economic importance, but has also rapidly degraded over the last 30 years. Improved spatial information on reef geomorphic zonation and benthic cover type (including coral type) is critical to support scientific work to understand how the GBR is changing, and to support resource management decisions that enable conservation of the reef and its essential ecosystem services. Yet, no comprehensive maps exist that detail the geomorphic zonation or benthic cover for the GBR’s ~ 3000 reefs. This study presents three new types of shallow reef maps for 237 reefs in the central Cairns Management Region of the GBR Marine Park (GBRMP), explores how the detailed habitat maps created compared to current maps and posits how the new maps may support and refine current critical key science outputs and management challenges. Geomorphic Zonation, Benthic Cover and Coral Type habitat maps were created using a unique combined object-based image analysis and ecological modelling approach that incorporated satellite imagery, limited field data and key reef physical attributes (depth, slope, waves) using a previously peer-reviewed mapping approach developed for the Capricorn Bunker Group reefs, Southern GBR. The mapping approach was consistent and repeatable, suggesting applicability to mapping all 3000 reefs in the GBRMP. Compared to existing maps that only outline each reef, the increase in detail provided by these new habitat maps enabled discrete characterisation of each reef’s geomorphology and benthic composition. With the new habitat maps, areas within each reef can be identified as either coral habitat or not coral habitat. This has not been possible previously. As such, a model of coral ecological and biophysical processes that depends on bottom reflectance of sand and coral areas can be fine-tuned. Similarly, for reef restoration, nursery structures are commonly placed in non-coral habitats, and/or coral larvae are dispersed in areas of known coral habitat. The new habitat maps presented permit more accurate identification of these areas such that restoration projects can be targeted more effectively. These findings confirm the need to now apply this mapping approach to the full extent of the GBR.

Mine sites are routinely required to rehabilitate their post-mining landforms with a safe, stable and sustainable land-cover. To assess these post-mining landforms, traditional on-ground field monitoring is generally undertaken. However, these labour intensive and time-consuming measurements are generally insufficient to catalogue land rehabilitation efforts across the large scales typical of mining sites (>100 ha). As an alternative, information derived from Unmanned Aerial Vehicles (UAV) can be used to map rehabilitation success and provide evidence of achieving rehabilitation site requirements across a range of scales. UAV based sensors have the capacity to collect information on rehabilitation sites with extensive spatial coverage in a repeatable, flexible and cost-effective manner. Here, we present an approach to automatically map indicators of safety, stability and sustainability of rehabilitation efforts, and demonstrate this framework across three coalmine sites. Using multi-spectral UAV imagery together with geographic object-based image analysis, an empirical classification system is proposed to convert these indicators into a status category based on a number of criteria related to land-cover, landform, erosion, and vegetation structure. For this study, these criteria include: mapping tall trees (Eucalyptus species); vegetation extent; senescent vegetation; extent of bare ground; and steep slopes. Converting these land-cover indicators into appropriate mapping categories on a polygon basis indicated the level of rehabilitation success and how these varied across sites and age of the rehabilitation activity. This work presents a framework and workflow for undertaking a UAV based assessment of safety, stability and sustainability of mine rehabilitation and also provides a set of recommendations for future rehabilitation assessment efforts.

The greyback canegrub (Dermolepida albohirtum) is the main pest of sugarcane crops in all cane-growing regions between Mossman (16.5°S) and Sarina (21.5°S) in Queensland, Australia. In previous yea...

With salt stress presenting a major threat to global food production, attention has turned to the identification and breeding of crop cultivars with improved salt tolerance. For instance, some accessions of wild species with higher salt tolerance than commercial varieties are being investigated for their potential to expand food production into marginal areas or to use brackish waters for irrigation. However, assessment of individual plant responses to salt stress in field trials is time-consuming, limiting, for example, longitudinal assessment of large numbers of plants. Developments in Unmanned Aerial Vehicle (UAV) sensing technologies provide a means for extensive, repeated and consistent phenotyping and have significant advantages over standard approaches. In this study, 199 accessions of the wild tomato species, Solanum pimpinellifolium, were evaluated through a field assessment of 600 control and 600 salt-treated plants. UAV imagery was used to: (1) delineate tomato plants from a time-series of eight RGB and two multi-spectral datasets, using an automated object-based image analysis approach; (2) assess four traits, i.e., plant area, growth rates, condition and Plant Projective Cover (PPC) over the growing season; and (3) use the mapped traits to identify the best-performing accessions in terms of yield and salt tolerance. For the first five campaigns, >99% of all tomato plants were automatically detected. The omission rate increased to 2–5% for the last three campaigns because of the presence of dead and senescent plants. Salt-treated plants exhibited a significantly smaller plant area (average control and salt-treated plant areas of 0.55 and 0.29 m2, respectively), maximum growth rate (daily maximum growth rate of control and salt-treated plant of 0.034 and 0.013 m2, respectively) and PPC (5–16% difference) relative to control plants. Using mapped plant condition, area, growth rate and PPC, we show that it was possible to identify eight out of the top 10 highest yielding accessions and that only five accessions produced high yield under both treatments. Apart from showcasing multi-temporal UAV-based phenotyping capabilities for the assessment of plant performance, this research has implications for agronomic studies of plant salt tolerance and for optimizing agricultural production under saline conditions.

The spatial arrangement and vertical structure of vegetation in urban green spaces are key factors in determining the types of benefits that urban parks provide to people. This includes opportunities for recreation, spiritual fulfilment and biodiversity conservation. However, there has been little consideration of how the fine-scale spatial and vertical structure of vegetation is distributed in urban parks, primarily due to limitations in methods for doing so. We addressed this gap by developing a method using Light Detection and Ranging (LiDAR) data to map, at a fine resolution, tree cover, vegetation spatial arrangement, and vegetation vertical structure. We then applied this method to urban parks in Brisbane, Australia. We found that parks varied mainly in their amount of tree cover and its spatial arrangement, but also in vegetation vertical structure. Interestingly, the vertical structure of vegetation was largely independent of its cover and spatial arrangement. This suggests that vertical structure may be being managed independently to tree cover to provide different benefits across urban parks with different levels of tree cover. Finally, we were able to classify parks into three distinct classes that explicitly account for both the spatial and vertical structure of tree cover. Our approach for mapping the three-dimensional vegetation structure of urban green space provides a much more nuanced and functional description of urban parks than has previously been possible. Future research is now needed to quantify the relationships between vegetation structure and the actual benefits people derive from urban green space.

Summary Drylands cover approximately one-third of the Earth’s surface, are home to nearly 40% of the Earth’s population and are characterised by limited water resources and ephemeral river systems with an extremely variable flow regime and high transmission losses. These losses include actual evaporation, infiltration to the soil and groundwater and residual (terminal) water remaining after flood events. These critical components of the water balance of dryland river systems remain largely unknown due to the scarcity of observational data and the difficulty in accurately accounting for the flow distribution in such large multi-channel floodplain systems. While hydrodynamic models can test hypotheses concerning the water balance of infrequent flood events, the scarcity of flow measurement data inhibits model calibration, constrains model accuracy and therefore utility. This paper provides a novel approach to this problem by combining modelling, remotely-sensed data, and limited field measurements, to investigate the partitioning of flood transmissions losses based on seven flood events between February 2006 and April 2012 along a 180 km reach of the Diamantina River in the Lake Eyre Basin, Australia. Transmission losses were found to be high, on average 46% of total inflow within 180 km reach segment or 7 GL/km (range: 4–10 GL/km). However, in 180 km reach, transmission losses vary non-linearly with flood discharge, with smaller flows resulting in higher losses (up to 68%), which diminish in higher flows (down to 24%) and in general there is a minor increase in losses with distance downstream. Partitioning these total losses into the major components shows that actual evaporation was the most significant component (21.6% of total inflow), followed by infiltration (13.2%) and terminal water storage (11.2%). Lateral inflow can be up to 200% of upstream inflow (mean = 86%) and is therefore a critical parameter in the water balance and transmission loss calculations. This study shows that it is possible to constrain the water balance using hydrodynamic models in dryland river systems using remote sensing and simple field measurements to address the otherwise scarce availability of data. The results of this study also enable a better understanding of the water resources available for ecosystems in these unique multi-channel and large floodplain rivers. The combined modelling/remote sensing approach of this study can be applied elsewhere in the world to better understand the water balances and water transmission losses, important drivers of ecohydrological processes in dryland environments.

Monitoring of vegetation clearing in Australia is the province of state governments. Only recently have data and services become available for generalised access to change detection tools suited to this task. The objective of this research was to examine if a globally available cloud computing service, Google Earth Engine Beta, could be used to predict decreases of woody vegetation with accuracies approaching those obtained by the government of the state of Queensland, Australia. This research compared the remote sensing results derived with the Google Earth Engine with those reported by the Queensland Government, using their standard remote sensing methods. Four change detection approaches were investigated using the Landsat-5 TM and 7 ETM+ time-series and algorithms available through the Google Earth Engine Application Programming Interface: (1) Classification and Regression Tree (CART) and (2) Random Forest classifiers; and a normalised time-series of (3) Normalised Difference Vegetation Index (NDVI) and (4) Foliage Projective Cover (FPC) combined with a spectral index, used to predict woody vegetation change between two image composites. The CART and Random Forest classifiers produced the highest user's (78–92%) and producer's (55–77%) mapping accuracies of clearing compared against the woody vegetation loss maps produced by the Queensland Government when detecting change within epochs for which training data were available. Extrapolation to epochs without training data reduced the mapping accuracies. The normalised FPC and NDVI time-series approaches were more robust for calculating clearing probability, as no training data were required, and can hence be tuned to provide automated alerts for large woody vegetation clearing events by selecting suitable thresholds. This research provides a foundation to build further capacity to use globally accessible, free, online image datasets and processing tools to detect woody vegetation clearing in an automated manner.

Effective mangrove management requires spatially explicit information of mangrove tree crown map as a basis for ecosystem diversity study and health assessment. Accuracy assessment is an integral part of any mapping activities to measure the effectiveness of the classification approach. In geographic object-based image analysis (GEOBIA) the assessment of the geometric accuracy (shape, symmetry and location) of the created image objects from image segmentation is required. In this study we used an explicit area-based accuracy assessment to measure the degree of similarity between the results of the classification and reference data from different aspects, including overall quality (OQ), user’s accuracy (UA), producer’s accuracy (PA) and overall accuracy (OA). We developed a rule set to delineate the mangrove tree crown using WorldView-2 pan-sharpened image. The reference map was obtained by visual delineation of the mangrove tree crowns boundaries form a very high-spatial resolution aerial photograph (7.5cm pixel size). Ten random points with a 10 m radius circular buffer were created to calculate the area-based accuracy assessment. The resulting circular polygons were used to clip both the classified image objects and reference map for area comparisons. In this case, the area-based accuracy assessment resulted 64% and 68% for the OQ and OA, respectively. The overall quality of the calculation results shows the class-related area accuracy; which is the area of correctly classified as tree crowns was 64% out of the total area of tree crowns. On the other hand, the overall accuracy of 68% was calculated as the percentage of all correctly classified classes (tree crowns and canopy gaps) in comparison to the total class area (an entire image). Overall, the area-based accuracy assessment was simple to implement and easy to interpret. It also shows explicitly the omission and commission error variations of object boundary delineation with colour coded polygons.

The Banana Bunchy Top Virus (Genus: Babuvirus) reduces plant growth and prevents banana production. Because of the very large number of properties with banana plants in South East Queensland, Australia, a mapping approach was developed to delineate individual and clusters of banana plants to help plant identification and enable prioritization of plant inspections for Banana Bunchy Top Virus. Due to current outbreaks in South East Queensland, there are concerns that the virus may spread to the major banana growing districts further north. The mapping approach developed was based on very high spatial resolution airborne orthophotos. Object-based image analysis was used to: (1) detect banana plants using edge and line detection approaches; (2) produce accurate and realistic outlines around classified banana plants; and (3) evaluate the mapping results. The mapping approach was developed based on 10 image tiles of 1 km × 1 km and was applied to orthophotos (3600 image tiles) from September 2011 covering the entire Sunshine Coast Region in South East Queensland. Based on field inspections of the classified maps, a user’s mapping accuracy of 88% (n = 146) was achieved. The results will facilitate the detection of banana plants and increase the inspection rate of Banana Bunchy Top Virus in the future.

Satellite altimeters have been launched with the objective to monitor changes in sea level and glacial ice sheet topography. More recently, their potential to monitor inland water bodies such as lakes, rivers and wetlands has been recognised. The objective of this study was to assess the accuracy of measuring surface water elevation changes of large multi-channel inland river systems using data from multiple altimetry satellite sensors and different retracking methods. We initially validated satellite altimetry data with in situ gauge data on Lake Argyle and Lake Eildon (Australia), and then investigated data of the only presently operational altimeter, Jason-2/Ocean Surface Topography Mission (OSTM), data at six locations on large inland rivers where temporary gauges were installed during 2011-2012 floods.Our analysis on Lake Argyle showed that the application of an alternative retracking algorithm significantly improved the agreement of altimeter and gauge data. We also found that the altimeter with the smallest footprint (50-90. m) and the highest along-track resolution (40. Hz, ~170. m), ICESat, provided more accurate lake water surface elevation measurements (mean. = 0.00. m, RMSE. = 0.04. m) than other altimeters, Jason-2 (mean. = -0.04. m, RMSE. = 0.28. m), Envisat (mean. = 0.25. m, RMSE. = 0.42. m), Jason-1 (mean. = -0.04. m, RMSE. = 1.07. m), GFO (mean. = 0.5. m, RMSE. = 0.89. m) and T/P (mean. = 0.77. m, RMSE. = 1.5. m).This study also investigated altimetry satellite data accuracy at six Jason-2/OSTM ground track sites crossing the Cooper/Diamantina Rivers where water level loggers were installed to collect data during the 2011-2012 floods (N.B. ICESat ceased operations in 2009). It is shown that satellite altimetry data is able to simulate moderate to major flood events in these large multi-channel inland river systems (R=0.90-0.98). Altimetry data further revealed a variation of water height in the channels across the river system. The general usefulness of satellite altimetry in hydrological applications in remote data sparse regions is confirmed, more specifically to study large multi-channel anabranching river systems such as the Cooper/Diamantina Rivers of the Lake Eyre Basin in Australia and similar systems worldwide where conventional gauging methods are difficult to use. This study also highlights the potential operational applications for monitoring inland flood wave characteristics and hydrodynamic behaviour of remote and multi-channel floodplain systems particularly using the still-operational Jason-2 platform.

Fringe mangroves are recognised as a highly valuable resource due to their provision of multiple ecosystem services. However, accelerating human-pressures on mangrove forests are resulting in the loss of key mangrove ecosystem services. In this paper, we assessed spatial-temporal changes in the extent and width of fringe mangroves, and changes in adjacent land use. The study focused on the Kien Giang Province, Vietnam, for the period 1989-2009. The total mangrove extent was estimated at seven points in time using four Landsat TM images (1989, 1992, 2003, and 2006) and three SPOT images (1995, 2003 and 2009). Supervised classification was conducted for image classification and mapping changes using a Maximum Livelihood Classifier approach. Changes in mangrove width were calculated for 2003 and 2009 using the Digital Shoreline Analysis Application.An assessment of the four Landsat TM images showed a significant decrease in mangrove extent for the periods 1989-1992 (-2.7% yr-1) and 2003-2006 (-2.1% yr-1), while a significant increase in mangrove extent was observed during 1992-2003 (0.7% yr-1). SPOT images were used to classify shoreline land cover into four types, namely mangroves; shrimp farms; built-up areas; and bare and wet ground areas. The total loss of fringe mangrove extent was significantly correlated with the increase in extent of other land covers, especially shrimp farms and bare and wet ground, during the period of 2003-2009.The overall accuracy assessment of the classification using 2003 and 2009 SPOT 5 images was 90.1% and 90.5% respectively. The overall accuracy assessment of the 2003 Landsat image was 92.1%. This accuracy in classification of mangroves and non-mangroves showed that use of Landsat images was adequate for assessing spatial-temporal mangrove dynamics when higher spatial resolution satellite images were limited. The results highlight the dynamic nature of fringe mangrove communities in the region, and provide data useful for informing shoreline mangrove rehabilitation, conservation efforts, and national monitoring programs for estimating mangrove values of carbon sequestration and shoreline protection functions in Vietnam.

Subsidence, resulting from underground coal mining can alter the structure of overlying rock formations changing hydrological conditions and potentially effecting ecological communities found on the surface. Of particular concern are impacts to endangered and/or protected swamp communities and swamp species sensitive to changes in hydrologic conditions. This paper describes a monitoring approach that uses UAVs with modified digital cameras and object-based image analysis methods to characterise swamp landcover on the Newnes plateau in the Blue Mountains near Sydney, Australia. The characterisation of swamp spatial distribution is key to identifying long term changes in swamp condition. In this paper we describe i) the characteristics of the UAV and the sensor, ii) the pre-processing of the remote sensing data with sub-decimeter pixel size to derive visible and near infrared multispectral imagery and a digital surface model (DSM), and iii) the application of object-based image analysis in eCognition using the multi-spectral data and DSM to map swamp extent. Finally, we conclude with a discussion of the potential application of remote sensing data derived from UAVs to conduct environmental monitoring.

Riparian zones in Australia are exposed to increasing pressures because of disturbance from agricultural and urban expansion, weed invasion, and overgrazing. Accurate and cost-effective mapping of riparian environments is important for assessing riparian zone functions associated with water quality, biodiversity, and wildlife habitats. The objective of this research was to compare the accuracy and costs of mapping riparian zone attributes from image data acquired by three different sensor types, i.e. Light Detection and Ranging (LiDAR) (0.5–2.4 m pixels), and multi-spectral QuickBird (2.4 m pixels) and SPOT-5 (10 m pixels). These attributes included streambed width, riparian zone width, plant projective cover, longitudinal continuity, vegetation overhang, and bank stability. The riparian zone attributes were mapped for a study area along Mimosa Creek in the Fitzroy Catchment, Central Queensland, Australia. Object-based image and regression analyses were used for mapping the riparian zone attributes. The validation of the LiDAR, QuickBird, and SPOT-5 derived maps of streambed width ( R = 0.99, 0.71, and 0.44 respectively) and riparian zone width ( R = 0.91, 0.87, and 0.74 respectively) against field derived measurements produced the highest accuracies for the LiDAR data and the lowest using the SPOT-5 image data. Cross-validation estimates of misclassification produced a root mean square error of 1.06, 1.35 and 1.51 from an ordinal scale from 0 to 4 of the bank stability score for the LiDAR, QuickBird and SPOT-5 image data, respectively. The validation and empirical modelling showed high correlations for all datasets for mapping plant projective cover ( R > 0.93). The SPOT-5 image data were unsuitable for assessment of riparian zone attributes at the spatial scale of Mimosa Creek and associated riparian zones. Cost estimates of image and field data acquisition and processing of the LiDAR, QuickBird, and SPOT-5 image data showed that discrete return LiDAR can be used for costs lower than those for QuickBird image data over large spatial extents (e.g. 26,000 km of streams). With the higher level of vegetation structural and landform information, mapping accuracies, geometric precision, and lower overall costs at large spatial extents, LiDAR data are a feasible means for assessment of riparian zone attributes.

Mapping, monitoring and managing the environmental condition of riparian zones are major focus areas for local and state governments in Australia. New remotely sensed data techniques that can provide the required mapping accuracies, complete spatial coverage and processing and mapping transferability are currently being developed for use over large spatial extents. The research objective was to develop and apply an approach for mapping riparian condition indicators using object-based image analysis of airborne Light Detection and Ranging (LiDAR) data. The indicators assessed were: streambed width; riparian zone width; plant projective cover (PPC); longitudinal continuity; coverage of large trees; vegetation overhang; and stream bank stability. LiDAR data were captured on 15 July 2007 for two 5 km stretches along Mimosa Creek in Central Queensland, Australia. Field measurements of riparian vegetation structural and landform parameters were obtained between 28 May and 5 June 2007. Object-based approaches were developed for mapping each riparian condition indicator from the LiDAR data. The validation and empirical modelling results showed that the object-based approach could be used to accurately map the riparian condition indicators (R2 = 0.99 for streambed width, R2 = 0.82 for riparian zone width, R2 = 0.89 for PPC, R2 = 0.40 for bank stability). These research findings will be used in a 26,000 km mapping project assessing riparian vegetation and physical form indicators from LiDAR data in Victoria, Australia.

The objectives of this research were to (a) develop a geo-object-based classification system for accurately mapping riparian land-cover classes for two QuickBird images, and (b) compare change maps derived from geo-object-based and per-pixel inputs used in three change detection techniques. The change detection techniques included post-classification comparison, image differencing, and the tasseled cap transformation. Two QuickBird images, atmospherically corrected to at-surface reflectance, were captured in May and August 2007 for a savanna woodlands area along Mimosa Creek in Central Queensland, Australia. Concurrent in-situ land-cover identification and lidar data were used for calibration and validation. The geo-object-based classification results showed that the use of class-related features and membership functions could be standardized for classifying the two QuickBird images. The geo-object-based inputs provided more accurate change detection results than those derived from the pixel-based inputs, as the geo-object-based approach reduced mis-registration and shadowing effects and allowed inclusion of context relationships.

The objectives of this research were: (1) to quantify indicators of riparian condition; and (2) to assess these indicators for detecting change in riparian condition. Two multi-spectral QuickBird images were acquired in 2004 and 2005 for a section of the Daly River in north Australia. These data were collected coincidently with vegetation and geomorphic field data. Indicators of riparian condition, including percentage canopy cover, organic litter, canopy continuity, bank stability, flood damage, riparian zone width and vegetation overhang, were then mapped. Field measurements and vegetation indices were empirically related using regression analysis to develop algorithms for mapping organic litter and canopy cover (R 2 = 0.59-0.78). Using a standard nearest-neighbour algorithm, object-oriented supervised image classification provided thematic information (overall accuracies 81-90%) for mapping riparian zone width and vegetation overhang. Bank stability and flood damage were mapped empirically from a combination of canopy cover information and the image classification products (R 2 = 0.70-0.81). Multi-temporal image analysis of riparian condition indicators (RCIs) demonstrated the advantages of using continuous and discrete data values as opposed to categorical data. This research demonstrates how remote sensing can be used for mapping and monitoring riparian zones in remote tropical savannas and other riparian environments at scales from 1 km to 100s km of stream length.

Measuring and monitoring mangrove Leaf Area Index (LAI) is an essential part of monitoring and management of mangrove environments. This study investigated the use of ALOS AVNIR-2 (AVNIR-2) image data for LAI estimation in mangrove environment through semi-empirical analysis, and compared and contrasted the results with different mangrove environmental settings and vegetation structures at sites in tropical Karimunjawa Island (KI, Indonesia) and sub-tropical Moreton Bay (MB, Australia). We used Normalized Difference Vegetation Index (NDVI) as a proxy to estimate LAI in both sites. The results indicated that the regression analyses showed significant coefficient of determination (R2) values of 0.82 at KI and 0.83 at MB. The range of LAI values for KI from 0.88 to 5.33 (n = 76, mean = 2.98, SD = 1.04) and for MB from 0.26 to 3.23 (n = 120, mean = 1.97, SD = 0.45). In terms of site comparison, we found that the results of LAI estimation from field measurement and remote sensing data varies across sites; the Karimunjawa Island mangroves had a comparatively wider range and variation of LAI values than the Moreton Bay mangroves. This difference was attributed to the difference in stand heterogeneity and species composition. Further investigation shows that mangrove LAI values are independent of the mangrove structural formation (i.e. scrub, low-closed forest, closed forest, etc.). Instead, the LAI value depends on the local spatial variation of mangrove phenological stages and canopy cover. The findings provide an understanding of the spatial variation of mangrove vegetation structure for estimating LAI.

Terrestrial Ecosystem Mapping provides critical information to land and resource managers by incorporating information on climate, physiography, surficial material, soil, and vegetation structure. The main objective of this research was to determine the capacity of high spatial resolution satellite image data to discriminate vegetation structural stages in riparian and adjacent forested ecosystems as defined using the British Columbia Terrestrial Ecosystem Mapping (TEM) scheme. A high spatial resolution QuickBird image, captured in June 2005, and coincident field data covering the riparian area of Lost Shoe Creek and adjacent forests on Vancouver Island, British Columbia, was used in this analysis. Semi-variograms were calculated to assess the separability of vegetation structural stages and assess which spatial scales were most appropriate for calculation of grey-level co-occurrence texture measures to maximize structural class separation. The degree of spatial autocorrelation showed that most vegetation structural types in the TEM scheme could be differentiated and that window sizes of 3 X 3 pixels and 11 X 11 pixels were most appropriate for image texture calculations. Using these window sizes, the texture analysis showed that co-occurrence contrast, dissimilarity, and homogeneity texture measures, based on the bands in the visible part of the spectrum, provided the most significant statistical differentiation between vegetation structural classes. Subsequently, an object-oriented classification algorithm was applied to spectral and textural transformations of the QuickBird image data to map the vegetation structural classes. Using both spectral and textural image bands yielded the highest classification accuracy (overall accuracy= 78.95%). The inclusion of image texture increased the classification accuracies of vegetation structure by 2-19%. The results show that information on vegetation structure can be mapped effectively from high spatial resolution satellite image data, providing an additional tool to ongoing aerial photograph interpretation. (c) 2007 Elsevier Inc. All rights reserved.

Riparian areas contain structurally diverse habitats that are challenging to monitor routinely and accurately over broad areas. As the structural variability within riparian areas is often indiscernible using moderate-scale satellite imagery, new mapping techniques are needed. We used high spatial resolution satellite imagery from the QuickBird satellite to map harvested and intact forests in coastal British Columbia, Canada. We distinguished forest structural classes used in riparian restoration planning, each with different restoration costs. To assess the accuracy of high spatial resolution imagery relative to coarser imagery, we coarsened the pixel resolution of the image, repeated the classifications, and compared results. Accuracy assessments produced individual class accuracies ranging from 70 to 90% for most classes; whilst accuracies obtained using coarser scale imagery were lower. We also examined the implications of map error on riparian restoration budgets derived from our classified maps. To do so, we modified the confusion matrix to create a cost error matrix quantifying costs associated with misclassification. High spatial resolution satellite imagery can be useful for riparian mapping; however, errors in restoration budgets attributable to misclassification error can be significant, even when using highly accurate maps. As the spatial resolution of imagery increases, it will be used more routinely in ecosystem ecology. Thus, our ability to evaluate map accuracy in practical, meaningful ways must develop further. The cost error matrix is one method that can be adapted for conservation and planning decisions in many ecosystems.

Suitable methods for measuring and monitoring the condition of riparian environments are being investigated by government agencies responsible for maintaining these environments in Australia. The objective of this work was to compare two riparian condition assessment approaches, the Tropical Rapid Appraisal of Riparian Condition (TRARC) method developed for rapid on-ground assessment of the environmental condition of savanna riparian zones and an image based riparian condition monitoring scheme. Measurements derived from these two approaches were compared and correlated. The sample representativeness of the TRARC method was evaluated and the cost-effectiveness and suitability for multi-temporal analysis of the two approaches were assessed. Two high spatial resolution multi-spectral QuickBird satellite 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 indicators of riparian zone condition including percentage canopy cover, organic litter on the ground, canopy continuity, tree clearing, bank stability, and flood damage. Spectral vegetation indices, image segmentation, and supervised classification were used to produce riparian health indicator maps. QuickBird image data were used to examine if the spatial distribution of TRARC transects provided a representative sample of ground based estimates of riparian health indicators. Covering approximately 3% of the study area, the sample mean of the TRARC estimates of individual indicators of riparian zone condition were in most cases within 20% of the global mean derived from the whole imaged riparian area. The cost-effectiveness of the image based approach was compared to that of the ground based TRARC method. Results showed that the TRARC method was more cost-effective at spatial scales from 1 km to 200 km of river in relatively homogeneous riparian zones along rivers with only one channel, while image based assessment becomes more feasible at regional scales (200-2000 km of river). A change detection analysis demonstrated that image data can provide detailed information on gradual change, while the TRARC method is less suited for multi-temporal analysis due to the ranked data format, which inhibits precise detection of change. However, results from both methods were considered to complement each other for single date assessment of riparian zones if used at appropriate spatial scales.

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 rip...

Government agencies responsible for riparian environments are assessing the utility of remote sensing for mapping and monitoring vegetation structural parameters. The objective of this work was to evaluate Ikonos and Landsat-7 ETM+ imagery for mapping structural parameters and species composition of riparian vegetation in Australian tropical savannahs for a section of Keelbottom Creek, Queensland, Australia. Vegetation indices and image texture from Ikonos data were used for estimating leaf area index (R-2 = 0.13) and canopy percentage foliage cover (R-2 = 0.86). Pan-sharpened Ikonos data were used to map riparian species composition (overall accuracy = 55 percent) and riparian zone width (accuracy within +/- 3 m). Tree crowns could not be automatically delineated due to the lack of contrast between canopies and adjacent grass cover. The ETM+ imagery was suited for mapping the extent of riparian zones. Results presented demonstrate the capabilities of high and moderate spatial resolution imagery for mapping properties of riparian zones.

Tropical forests are being cleared at alarming rates. The release of the Landsat image archive represents an opportunity to assess rainforest clearing over time through time-series analysis. The objective was to map the extent of rainforest clearing and assess land cover trends at the object level within a selected study area in Sabah, Borneo using Landsat images from 1991, 2000, 2004 and 2008. The images were delineated based on image interpretation cues and validated against existing high spatial resolution images on Google Earth. Overall mapping accuracies were >94%. Time-series trends for each delineated object were classified into trend classes to quantify the land cover history per object. The results showed that approximately 31% or 5,500 km2 of land cover within the study area changed between 1991 and 2008. This research presents an effective method for time-series analysis that can be used to regularly monitor forest clearing on Borneo.

Government agencies responsible for riparian environments are assessing the utility of remote sensing for mapping and monitoring environmental health indicators. The objective of this work was to evaluate IKONOS and Landsat-7 ETM+ imagery for mapping riparian vegetation health indicators in tropical savannas for a section of Keelbottom Creek, Queensland, Australia. Vegetation indices and image texture from IKONOS data were used for estimating percentage canopy cover (r2=0.86). Pan-sharpened IKONOS data were used to map riparian species composition (overall accuracy=55%) and riparian zone width (accuracy within 4 m). Tree crowns could not be automatically delineated due to the lack of contrast between canopies and adjacent grass cover. The ETM+ imagery was suited for mapping the extent of riparian zones. Results presented demonstrate the capabilities of high and moderate spatial resolution imagery for mapping properties of riparian zones, which may be used as riparian environmental health indicators

Stream bank condition is an important physical form indicator for streams related to the environmental condition of riparian corridors. This research developed and applied an approach for mapping bank condition from airborne light detection and ranging (LiDAR) and high-spatial resolution optical image data in a temperate forest/woodland/urban environment. Field observations of bank condition were related to LiDAR and optical image-derived variables, including bank slope, plant projective cover, bank-full width, valley confinement, bank height, bank top crenulation, and ground vegetation cover. Image-based variables, showing correlation with the field measurements of stream bank condition, were used as input to a cumulative logistic regression model to estimate and map bank condition. The highest correlation was achieved between field-assessed bank condition and image-derived average bank slope (R2 1/4 0.60, n 1/4 41), ground vegetation cover (R2 1/4 0.43, n 1/4 41), bank width/height ratio (R2 1/4 0.41, n 1/4 41), and valley confinement (producer's accuracy 1/4 100%, n 1/4 9). Crossvalidation showed an average misclassification error of 0.95 from an ordinal scale from 0 to 4 using the developed model. This approach was developed to support the remotely sensed mapping of stream bank condition for 26,000 km of streams in Victoria, Australia, from 2010 to 2012.

This research presents a time-effective approach for mapping streambed and riparian zone extent from high spatial resolution LiDAR derived products, i.e., digital terrain model, terrain slope and plant projective cover. Geographic object based image analysis (GEOBIA) has proven useful for feature extraction from high spatial resolution image data because of the capacity to reduce effects of reflectance variations of pixels making up individual objects and to include contextual and shape information. This functionality increases the likelihood of developing transferable and automated mapping approaches. LiDAR data covered parts of the Werribee Catchment in Victoria, Australia, which is characterized by urban, agricultural, and forested land cover types. Field data of streamside vegetation structure and physical form properties were used for both calibration of the mapping routines and validation of the mapping results. To improve the transferability of the rule set, the GEOBIA approach was developed for an area representing different riparian zone environments, i.e., urbanized, agricultural and hilly forested areas. Results show that mapping streambed extent (R2 = 0.93, RMSE = 3.6 m, n = 35) and riparian zone extent (R2 = 0.74, RMSE = 3.9, n = 35) from LiDAR derived products can be automated using GEOBIA to enable derivation of spatial information in an accurate and time-effective manner suited for natural resource management agencies. (VLID)2190213