ORCID Profile
0000-0002-7071-6667
Current Organisations
CSIRO
,
The University of Newcastle
,
Aberystwyth University
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Publisher: Informa UK Limited
Date: 14-10-2022
Publisher: MDPI AG
Date: 25-02-2021
DOI: 10.3390/RS13050846
Abstract: National-level mapping of crop types is important to monitor food security, understand environmental conditions, inform optimal use of the landscape, and contribute to agricultural policy. Countries or economic regions currently and increasingly use satellite sensor data for classifying crops over large areas. However, most methods have been based on machine learning algorithms, with these often requiring large training datasets that are not always available and may be costly to produce or collect. Focusing on Wales (United Kingdom), the research demonstrates how the knowledge that the agricultural community has gathered together over past decades can be used to develop algorithms for mapping different crop types. Specifically, we aimed to develop an alternative method for consistent and accurate crop type mapping where cloud cover is quite persistent and without the need for extensive in situ/ground datasets. The classification approach is parcel-based and informed by concomitant analysis of knowledge-based crop growth stages and Sentinel-1 C-band SAR time series. For 2018, crop type classifications were generated nationally for Wales, with regional overall accuracies ranging between 85.8% and 90.6%. The method was particularly successful in distinguishing barley from wheat, which is a major source of error in other crop products available for Wales. This study demonstrates that crops can be accurately identified and mapped across a large area (i.e., Wales) using Sentinel-1 C-band data and by capitalizing on knowledge of crop growth stages. The developed algorithm is flexible and, compared to the other methods that allow crop mapping in Wales, the approach provided more consistent discrimination and lower variability in accuracies between classes and regions.
Publisher: Elsevier BV
Date: 04-2020
Publisher: Wiley
Date: 09-2022
DOI: 10.1111/GCB.16346
Abstract: A globally relevant and standardized taxonomy and framework for consistently describing land cover change based on evidence is presented, which makes use of structured land cover taxonomies and is underpinned by the Driver-Pressure-State-Impact-Response (DPSIR) framework. The Global Change Taxonomy currently lists 246 classes based on the notation 'impact (pressure)', with this encompassing the consequence of observed change and associated reason(s), and uses scale-independent terms that factor in time. Evidence for different impacts is gathered through temporal comparison (e.g., days, decades apart) of land cover classes constructed and described from Environmental Descriptors (EDs state indicators) with pre-defined measurement units (e.g., m, %) or categories (e.g., species type). Evidence for pressures, whether abiotic, biotic or human-influenced, is similarly accumulated, but EDs often differ from those used to determine impacts. Each impact and pressure term is defined separately, allowing flexible combination into 'impact (pressure)' categories, and all are listed in an openly accessible glossary to ensure consistent use and common understanding. The taxonomy and framework are globally relevant and can reference EDs quantified on the ground, retrieved/classified remotely (from ground-based, airborne or spaceborne sensors) or predicted through modelling. By providing capacity to more consistently describe change processes-including land degradation, desertification and ecosystem restoration-the overall framework addresses a wide and erse range of local to international needs including those relevant to policy, socioeconomics and land management. Actions in response to impacts and pressures and monitoring towards targets are also supported to assist future planning, including impact mitigation actions.
Publisher: Elsevier BV
Date: 04-2022
Publisher: Informa UK Limited
Date: 25-07-2016
Publisher: Elsevier BV
Date: 04-2021
Publisher: Elsevier BV
Date: 10-2018
Publisher: Wiley
Date: 11-01-2022
DOI: 10.1002/LNO.12014
Abstract: The development and refinement of methods for estimating organic carbon accumulation in biomass and soils during mangrove restoration and rehabilitation can encourage uptake of restoration projects for their ecosystem services, including those of climate change mitigation, or blue carbon. To support the development of a blue carbon method for Australia under the Emission Reduction Fund scheme we investigated (1) whether carbon accumulation data from natural mangroves could be used to estimate carbon accumulation during restoration (2) modeling mangrove biomass accumulation and (3) how modeled carbon accumulation could be achieved over heterogeneous sites. First, we assessed carbon accumulation in soil and biomass pools from the global literature, finding that estimating carbon accumulation using data from natural mangroves provided similar estimates as those for restored or rehabilitated mangroves. We assessed mangrove biomass accumulation from global chronosequence studies, which we used to develop regional models for estimating biomass accumulation with restoration in Australia using values from local natural mangroves. Estimating biomass carbon accumulation using site‐based means of stand biomass provided similar estimates as values estimated through use of regional means values stratified by elevation and reduced overestimates of biomass carbon accumulation that were based on regional mean values. Modeling soil carbon accumulation over environmentally heterogeneous project sites can apply a similar approach, stratifying over variation in site elevation. Our analysis provides a strategy for modeling blue carbon pools for an Australian blue carbon method that accommodates regional differences and is based on data from natural mangroves.
Publisher: SPIE
Date: 20-09-2020
DOI: 10.1117/12.2573763
Publisher: SAGE Publications
Date: 10-10-2023
Publisher: SPIE
Date: 20-09-2020
DOI: 10.1117/12.2574005
Publisher: Elsevier BV
Date: 12-2021
Publisher: Coastal Education and Research Foundation
Date: 03-03-2016
DOI: 10.2112/SI75-260.1
Publisher: Informa UK Limited
Date: 03-07-2021
Publisher: Elsevier BV
Date: 05-2018
Publisher: MDPI AG
Date: 11-2019
DOI: 10.3390/DATA4040143
Abstract: This study establishes the use of the Earth Observation Data for Ecosystem Monitoring (EODESM) to generate land cover and change classifications based on the United Nations Food and Agriculture Organisation (FAO) Land Cover Classification System (LCCS) and environmental variables (EVs) available within, or accessible from, Geoscience Australia’s (GA) Digital Earth Australia (DEA). Classifications representing the LCCS Level 3 taxonomy (8 categories representing semi-(natural) and/or cultivated/managed vegetation or natural or artificial bare or water bodies) were generated for two time periods and across four test sites located in the Australian states of Queensland and New South Wales. This was achieved by progressively and hierarchically combining existing time-static layers relating to (a) the extent of artificial surfaces (urban, water) and agriculture and (b) annual summaries of EVs relating to the extent of vegetation (fractional cover) and water (hydroperiod, intertidal area, mangroves) generated through DEA. More detailed classifications that integrated information on, for ex le, forest structure (based on vegetation cover (%) and height (m) time-static for 2009) and hydroperiod (months), were subsequently produced for each time-step. The overall accuracies of the land cover classifications were dependent upon those reported for the in idual input layers, with these ranging from 80% (for cultivated, urban and artificial water) to over 95% (for hydroperiod and fractional cover). The changes identified include mangrove dieback in the southeastern Gulf of Carpentaria and reduced dam water levels and an associated expansion of vegetation in Lake Ross, Burdekin. The extent of detected changes corresponded with those observed using time-series of RapidEye data (2014 to 2016 for the Gulf of Carpentaria) and Google Earth imagery (2009–2016 for Lake Ross). This use case demonstrates the capacity and a conceptual framework to implement EODESM within DEA and provides countries using the Open Data Cube (ODC) environment with the opportunity to routinely generate land cover maps from Landsat or Sentinel-1/2 data, at least annually, using a consistent and internationally recognised taxonomy.
Publisher: CSIRO Publishing
Date: 2014
DOI: 10.1071/WR14168
Abstract: Context Hollow-bearing trees are an important breeding and shelter resource for wildlife in Australian native forests and hollow availability can influence species abundance and ersity in forest ecosystems. A persistent problem for forest managers is the ability to locate and survey hollow-bearing trees with a high level of accuracy at low cost over large areas of forest. Aims The aim of this study was to determine whether remote-sensing techniques could identify key variables useful in classifying the likelihood of a tree to contain hollows suitable for wildlife. Methods The data were high-resolution, multispectral aerial imagery and light detection and ranging (Lidar). A ground-based survey of 194 trees, 96 Eucalyptus crebra and 98 E. chloroclada and E. blakelyi, were used to train and validate tree-senescence classification models. Key results We found that trees in the youngest stage of tree senescence, which had a very low probability of hollow occurrence, could be distinguished using multispectral aerial imagery from trees in the later stages of tree senescence, which had a high probability of hollow occurrence. Independently, the canopy-height model used to estimate crown foliage density demonstrated the potential of Lidar-derived structural parameters as predictors of senescence and the hollow-bearing status of in idual trees. Conclusions This study demonstrated a ‘proof of concept’ that remotely sensed tree parameters are suitable predictor variables for the hollow-bearing status of an in idual tree. Implications Distinguishing early stage senescence trees from later-stage senescence trees using remote sensing offers potential as an efficient, repeatable and cost-effective way to map the distribution and abundance of hollow-bearing trees across the landscape. Further development is required to automate this process across the landscape, particularly the delineation of tree crowns. Further improvements may be obtained using a combination of these remote-sensing techniques. This information has important applications in commercial forest inventory and in bio ersity monitoring programs.
Publisher: Wiley
Date: 15-10-2022
Location: Australia
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Christopher Owers.