Irina Emelyanova

Blending landsat and MODIS data to generate multispectral indices: A comparison of "index-then-blend" and "Blend-Then-Index" approaches

The objective of this paper was to evaluate the accuracy of two advanced blending algorithms, Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to downscale Moderate Resolution Imaging Spectroradiometer (MODIS) indices to the spatial resolution of Landsat. We tested two approaches: (i) “Index-then-Blend” (IB); and (ii) “Blend-then-Index” (BI) when simulating nine indices, which are widely used for vegetation studies, environmental moisture assessment and standing water identification. Landsat-like indices, generated using both IB and BI, were simulated on 45 dates in total from three sites. The outputs were then compared with indices calculated from observed Landsat data and pixel-to-pixel accuracy of each simulation was assessed by calculating the: (i) bias; (ii) R2; and (iii) Root Mean Square Deviation (RMSD). The IB approach produced higher accuracies than the BI approach for both blending algorithms for all nine indices at all three sites. We also found that the relative performance of the STARFM and ESTARFM algorithms depended on the spatial and temporal variances of the Landsat-MODIS input indices. Our study suggests that the IB approach should be implemented for blending of environmental indices, as it was: (i) less computationally expensive due to blending single indices rather than multiple bands; (ii) more accurate due to less error propagation; and (iii) less sensitive to the choice of algorithm.

A spatial assessment framework for evaluating flood risk under extreme climates.

Australian coal mines have been facing a major challenge of increasing risk of flooding caused by intensive rainfall events in recent years. In light of growing climate change concerns and the predicted escalation of flooding, estimating flood inundation risk becomes essential for understanding sustainable mine water management in the Australian mining sector. This research develops a spatial multi-criteria decision making prototype for the evaluation of flooding risk at a regional scale using the Bowen Basin and its surroundings in Queensland as a case study. Spatial gridded data, including climate, hydrology, topography, vegetation and soils, were collected and processed in ArcGIS. Several indices were derived based on time series of observations and spatial modeling taking account of extreme rainfall, evapotranspiration, stream flow, potential soil water retention, elevation and slope generated from a digital elevation model (DEM), as well as drainage density and proximity extracted from a river network. These spatial indices were weighted using the analytical hierarchy process (AHP) and integrated in an AHP-based suitability assessment (AHP-SA) model under the spatial risk evaluation framework. A regional flooding risk map was delineated to represent likely impacts of criterion indices at different risk levels, which was verified using the maximum inundation extent detectable by a time series of remote sensing imagery. The result provides baseline information to help Bowen Basin coal mines identify and assess flooding risk when making adaptation strategies and implementing mitigation measures in future. The framework and methodology developed in this research offers the Australian mining industry, and social and environmental studies around the world, an effective way to produce reliable assessment on flood risk for managing uncertainty in water availability under climate change.

Delineation of riparian vegetation from Landsat multi‐temporal imagery using PCA

A deficiency in crucial digital data, such as vegetation cover, in remote regions is a challenging issue for water management and planning, especially for areas undergoing rapid development, such as mining in the Pilbara, Western Australia. This is particularly relevant to riparian vegetation, which provides important ecological services and, as such, requires regional protection. The objective of this research was to develop an approach to riparian vegetation mapping at a regional scale using remotely sensed data. The proposed method was based on Principal Component Analysis (PCA) applied to multi-temporal Normalised Difference Vegetation Index (NDVI) datasets derived from Landsat TM 5 imagery. To delimit the spatial extent of riparian vegetation, a thresholding method was required and various thresholding algorithms were tested. The accuracy of results was estimated for various NDVI multi-temporal datasets using available ground-truth data. The combination of a 14-dry-date dataset and Kittlers thresholding method provided the most accurate delineation of riparian vegetation. This article is protected by copyright. All rights reserved.

Large-scale regional delineation of riparian vegetation in the arid and semi-arid Pilbara region, WA

Multi-scene Landsat 5 TM imagery, Principal Component Analysis (PCA), and the Normalised Difference Vegetation Index (NDVI) were used to produce the first region-scale map of riparian vegetation for the Pilbara (230,000 km2), Western Australia. Riparian vegetation is an environmentally important habitat in the arid and desert climate of the Pilbara. These habitats are supported by infrequent flow events and in some locations by groundwater discharge. Our analysis suggests that riparian vegetation covers less than 4% of the Pilbara region, while almost 10.5% of this area is comprised of Groundwater Dependent Vegetation (GDV). GDV is often associated with open water (river pools), providing refugia for a variety of species. GDV has an extremely high ecological value and are often important Indigenous sites. This paper demonstrates how Landsat data calibrated to Top of Atmosphere (TOA) reflectance can be used to delineate riparian vegetation across 16 Landsat Scenes and two UTM (Universal Transverse Mercator) spatial zones. The proposed method is able to delineate riparian vegetation and GDV, without the need for Bidirectional Reflectance Distribution Function (BRDF) correction. Results were validated using ground-truth data from local and regional scale vegetation surveys.

Multivariate detection and attribution of land-cover changes in the Central Pilbara, Western Australia

The Multivariate Alteration Detection (MAD) method was applied to locate areas where land-cover changes occurred between 2003 and 2009 in the Central Pilbara, Western Australia. It was demonstrated that each of the six MAD variates contained information of land-cover changes at various spatial scales. This allowed attribution of the identified changes to particular stressors such as climate variability, fire events, and mining activity in the area. The results were analysed and interpreted using time series of multispectral normalized difference vegetation index, normalized difference wetness index, and normalized burn ratio grids derived from Landsat data observed over the study period. In addition, various ground truth data such as fire maps, historical climate data, and the available information about mine operations and water management, which could lead to alteration of natural water regime, were utilized.

A comparative evaluation of arid inflow-dependent vegetation maps derived from LANDSAT top-of-atmosphere and surface reflectances

ABSTRACT In remote sensing, it is commonly accepted that land remote-sensing satellite (LANDSAT) top-of-atmosphere (TOA) reflectance is less accurate than atmospheric correction (AC) reflectance, as the former is not calibrated for possible modifications in the electromagnetic radiation signals due to atmospheric scattering and absorption. This article investigates whether LANDSAT data calibrated for TOA reflectance are an appropriate information source for delineating inflow-dependent vegetation (IDV) in regions with an arid and desert climate, such as the Pilbara region in Western Australia. Knowledge of where IDVs are in the landscape underpins planning their protection and define the baseline for their monitoring when water resource management options are considered. The appropriateness of TOA calibration for the delineation of IDV in the Pilbara was assessed through its comparison with IDV maps derived from AC reflectance. Both radiometric calibration methods (TOA and AC) were applied to a multi-date LANDSAT 5 TM (Thematic Mapper) dataset of 10 images acquired in 2009 and 2010. Two methods based on the application of remote-sensing techniques to identify the extent of temporally invariant vegetation were applied for IDV delineation in the study area. The first method, groundwater-dependent ecosystems mapping (GEM), employs a two-date normalized difference vegetation index (NDVI) dataset for identifying ‘no-change’ clusters of land cover and detecting those related to IDV. The second method applies principal component analysis (PCA) to a multi-date NDVI dataset. The first principal component (PC1) typically contains features that remain unchanged over time. This includes vegetation with continuous or frequent access to surface and/or groundwater, such as IDV. To delineate the extent of IDV, a thresholding technique was further employed. Spatial similarity between IDV maps produced from TOA and AC reflectance was quantitatively evaluated by the Kappa coefficient. The results showed that TOA and AC IDV maps are in ‘almost perfect’ agreement with the Kappa values above 0.83. This suggests that TOA reflectance is equally appropriate to AC reflectance for mapping in arid and desert climate such as in Pilbara. When the GEM- and PCA-based methods are applied in other study areas with arid or desert climate, the accuracy of the delineated IDV extent may vary. Therefore, the results need to be validated using ground-truth information about known IDV occurrences in the area of interest.