ORCID Profile
0000-0001-9144-8027
Current Organisation
University of Adelaide
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Publisher: MDPI AG
Date: 28-11-2019
DOI: 10.3390/RS11232825
Abstract: The collection of high-quality field measurements of ground cover is critical for calibration and validation of fractional ground cover maps derived from satellite imagery. Field-based hyperspectral ground cover s ling is a potential alternative to traditional in situ techniques. This study aimed to develop an effective s ling design for spectral ground cover surveys in order to estimate fractional ground cover in the Australian arid zone. To meet this aim, we addressed two key objectives: (1) Determining how spectral surveys and traditional step-point s ling compare when conducted at the same spatial scale and (2) comparing these two methods to current Australian satellite-derived fractional cover products. Across seven arid, sparsely vegetated survey sites, six 500-m transects were established. Ground cover reflectance was recorded taking continuous hyperspectral readings along each transect while step-point surveys were conducted along the same transects. Both measures of ground cover were converted into proportions of photosynthetic vegetation, non-photosynthetic vegetation, and bare soil for each site. Comparisons were made of the proportions of photosynthetic vegetation, non-photosynthetic vegetation, and bare soil derived from both in situ methods as well as MODIS and Landsat fractional cover products. We found strong correlations between fractional cover derived from hyperspectral and step-point s ling conducted at the same spatial scale at our survey sites. Comparison of the in situ measurements and image-derived fractional cover products showed that overall, the Landsat product was strongly related to both in situ methods for non-photosynthetic vegetation and bare soil whereas the MODIS product was strongly correlated with both in situ methods for photosynthetic vegetation. This study demonstrates the potential of the spectral transect method, both in its ability to produce results comparable to the traditional transect measures, but also in its improved objectivity and relative logistic ease. Future efforts should be made to include spectral ground cover s ling as part of Australia’s plan to produce calibration and validation datasets for remotely sensed products.
Publisher: MDPI AG
Date: 04-11-2019
DOI: 10.3390/RS11212589
Abstract: Remotely sensed ground cover maps are routinely validated using field data collected by observers who classify ground cover into defined categories such as photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), bare soil (BS), and rock. There is an element of subjectivity to the classification of PV and NPV, and classifications may differ between observers. An alternative is to estimate ground cover based on in situ hyperspectral reflectance measurements (HRM). This study examines observer consistency when classifying vegetation s les of wheat (Triticum aestivum var. Gladius) covering the full range of photosynthetic activity, from completely senesced (0% PV) to completely green (100% PV), as photosynthetic or non-photosynthetic. We also examine how the classification of spectra of the same vegetation s les compares to the observer results. We collected HRM and photographs, over two months, to capture the transition of wheat leaves from 100% PV to 100% NPV. To simulate typical field methodology, observers viewed the photographs and classified each leaf as either PV or NPV, while spectral unmixing was used to decompose the HRM of the leaves into proportions of PV and NPV. The results showed that when a leaf was ≤25% or ≥75% PV observers tended to agree, and assign the leaf to the expected category. However, as leaves transitioned from PV to NPV (i.e., PV ≥ 25% but ≤ 75%) observers’ decisions differed more widely and their classifications showed little agreement with the spectral proportions of PV and NPV. This has significant implications for the reliability of data collected using binary methods in areas containing a significant proportion of vegetation in this intermediate range such as the over/underestimation of PV and NPV vegetation and how reliably this data can then be used to validate remotely sensed products.
Publisher: Elsevier BV
Date: 06-2018
Publisher: Elsevier BV
Date: 09-2018
DOI: 10.1016/J.SCITOTENV.2018.04.149
Abstract: Catchment properties influence the character and concentration of dissolved organic matter (DOM). Surface and subsurface runoff from discrete catchments were collected and DOM was measured and assessed in terms of its treatability by Enhanced Coagulation and potential for disinfection by-product (trihalomethane, THMFP) formation potential. Models were developed of [1] DOM character [i.e. SUVA and SpCoL] and concentration (measured as dissolved organic carbon), [2] treatability of DOM by coagulation/flocculation processes and [3] specific THMFP based on the catchment features including: (a) surface and sub-surface soil texture (% clay: 5-25%), (b) topography (% slope: 5-15%) and (c) vegetation cover [i.e. high photosynthetic vegetation, low photosynthetic vegetation and bare soil] extracted from RapidEye satellite imagery using spectral mixture analysis. From these models, a catchment management decision support tool was designed for application by catchment managers to support decision-making of land-use and expected water quality related to water resources for drinking water supply. Data sets used for models developing presented in this paper have been published in Research Data Australia (RDA) under the title of "Impacts of catchment properties on DOM and nutrients in waters from drinking water catchments".
No related grants have been discovered for Claire Fisk.