Richard Lucas

Professor Richard Lucas

• Remote sensing and biogeography
• Terrestrial ecosystem response to change
• Vegetation carbon dynamics
• Land cover and change mapping

The primary area of Professor Lucas’ expertise is in quantifying and understanding the response of terrestrial ecosystems and environments to change, including that associated with climatic variation, through integration of remote sensing data from various sources. He has also developed methods for extracting relevant information on terrestrial ecosystems at scales ranging from individual trees to entire regions. Key achievements include the development of object-based methods to update a national classification of habitats in Wales (UK) and a generic scheme for classifying land covers at any location and at multiple scales based on the FAO Land Cover Classification System (LCCS); implementing approaches to quantifying the biomass and structure of forests and woodlands in eastern Australia through integration of radar, optical and ICESAT data; and quantifying mangrove response to both natural and anthropogenic change, including that associated with climatic fluctuation. His collaborative research on lidar and hyperspectral data has established innovative techniques for characterising the species composition, structure and biomass of Australian woodlands at the tree and stand level and quantifying change.

Professor Lucas’ has developed a range of techniques for retrieving information on the state and dynamnics of terrestrial vegetation and his current research is increasingly focusing on using these to better understand the impacts of human-induced and natural change on a diversity of ecosystems, including mangroves, semi-arid woodlands and tropical rainforests. His research is also establishing how time-series of optical and radar remote sensing data can be used to restore previously lost or degraded ecosystems for the benefit of biodiversity conservation and carbon preservation and sequestration.


Author Date Title Link PDF
Murray et al. 2018 The role of satellite remote sensing in structured ecosystem risk assessments

Abstract: The current set of global conservation targets requires methods for monitoring the changing status of ecosystems. Protocols for ecosystem risk assessment are uniquely suited to this task, providing objective syntheses of a wide range of data to estimate the likelihood of ecosystem collapse. Satellite remote sensing can deliver ecologically relevant, long-term datasets suitable for analysing changes in ecosystem area, structure and function at temporal and spatial scales relevant to risk assessment protocols. However, there is considerable uncertainty about how to select and effectively utilise remotely sensed variables for risk assessment. Here, we review the use of satellite remote sensing for assessing spatial and functional changes of ecosystems, with the aim of providing guidance on the use of these data in ecosystem risk assessment. We suggest that decisions on the use of satellite remote sensing should be made a priori and deductively with the assistance of conceptual ecosystem models that identify the primary indicators representing the dynamics of a focal ecosystem.

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Murray et al. 2018 REMAP: A cloud-based remote sensing application for generalized ecosystem classifications.

Recent assessments of progress towards global conservation targets have revealed a paucity of indicators suitable for assessing the changing state of ecosystems. Moreover, land managers and planners are often unable to gain timely access to the maps they need to support their routine decision‐making. This deficiency is partly due to a lack of suitable data on ecosystem change, driven mostly by the considerable technical expertise needed to develop ecosystem maps from remote sensing data.
We have developed a free and open‐access online remote sensing and environmental modelling application, the Remote Ecosystem Monitoring and Assessment Pipeline (Remap;, that enables volunteers, managers and scientists with little or no experience in remote sensing to generate classifications (maps) of land cover and land use change over time.
Remap utilizes the geospatial data storage and analysis capacity of Google Earth Engine and requires only spatially resolved training data that define map classes of interest (e.g. ecosystem types). The training data, which can be uploaded or annotated interactively within Remap, are used in a random forest classification of up to 13 publicly available predictor datasets to assign all pixels in a focal region to map classes. Predictor datasets available in Remap represent topographic (e.g. slope, elevation), spectral (archival Landsat image composites) and climatic variables (precipitation, temperature) that are relevant to the distribution of ecosystems and land cover classes.
The ability of Remap to develop and export high‐quality classified maps in a very short (<10 min) time frame represents a considerable advance towards globally accessible and free application of remote sensing technology. By enabling access to data and simplifying remote sensing classifications, Remap can catalyse the monitoring of land use and change to support environmental conservation, including developing inventories of biodiversity, identifying hotspots of ecosystem diversity, ecosystem‐based spatial conservation planning, mapping ecosystem loss at local scales and supporting environmental education initiatives.

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Skidmore et al. 2015 Environmental science: Agree on biodiversity metrics to track from space

Conservation scientists should collaborate more with space agencies, such as NASA and the European Space Agency (ESA), on identifying measures to help track biodiversity declines around the world. For full publication click here.

Lucas et al. 2014 Mapping forest growth and degradation stage in the Brigalow Belt Bioregion of Australia through integration of ALOS PALSAR and Landsat-derived Foliage Projective Cover (FPC) data
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