Chris Roelfsema

Bio: Chris Roelfsema is an academic researcher from University of Queensland. The author has contributed to research in topics: Reef & Coral reef. The author has an hindex of 37, co-authored 159 publications receiving 6241 citations. Previous affiliations of Chris Roelfsema include Charles Darwin University & Cooperative Research Centre.

Australian vegetated coastal ecosystems as global hotspots for climate change mitigation

Abstract: Policies aiming to preserve vegetated coastal ecosystems (VCE; tidal marshes, mangroves and seagrasses) to mitigate greenhouse gas emissions require national assessments of blue carbon resources. Here, we present organic carbon (C) storage in VCE across Australian climate regions and estimate potential annual CO2 emission benefits of VCE conservation and restoration. Australia contributes 5–11% of the C stored in VCE globally (70–185 Tg C in aboveground biomass, and 1,055–1,540 Tg C in the upper 1 m of soils). Potential CO2 emissions from current VCE losses are estimated at 2.1–3.1 Tg CO2-e yr-1, increasing annual CO2 emissions from land use change in Australia by 12–21%. This assessment, the most comprehensive for any nation to-date, demonstrates the potential of conservation and restoration of VCE to underpin national policy development for reducing greenhouse gas emissions. Policies aiming to preserve vegetated coastal ecosystems (VCE) to mitigate greenhouse gas emissions require national assessments of blue carbon resources. Here the authors assessed organic carbon storage in VCE across Australian and the potential annual CO2 emission benefits of VCE conservation and find that Australia contributes substantially the carbon stored in VCE globally.

Remote Sensing of Coral Reefs for Monitoring and Management: A Review

Abstract: Coral reefs are in decline worldwide and monitoring activities are important for assessing the impact of disturbance on reefs and tracking subsequent recovery or decline. Monitoring by field surveys provides accurate data but at highly localised scales and so is not cost-effective for reef scale monitoring at frequent time points. Remote sensing from satellites is an alternative and complementary approach. While remote sensing cannot provide the level of detail and accuracy at a single point than a field survey, the statistical power for inferring large scale patterns benefits in having complete areal coverage. This review considers the state of the art of coral reef remote sensing for the diverse range of objectives relevant for management, ranging from the composition of the reef: physical extent, benthic cover, bathymetry, rugosity; to environmental parameters: sea surface temperature, exposure, light, carbonate chemistry. In addition to updating previous reviews, here we also consider the capability to go beyond basic maps of habitats or environmental variables, to discuss concepts highly relevant to stakeholders, policy makers and public communication: such as biodiversity, environmental threat and ecosystem services. A clear conclusion of the review is that advances in both sensor technology and processing algorithms continue to drive forward remote sensing capability for coral reef mapping, particularly with respect to spatial resolution of maps, and synthesis across multiple data products. Both trends can be expected to continue.

Intercomparison of shallow water bathymetry, hydro‐optics, and benthos mapping techniques in Australian and Caribbean coastal environments

Abstract: Science, resource management, and defense need algorithms capable of using airborne or satellite imagery to accu rately map bathymetry, water quality, and substrate composition in optically shallow waters. Although a variety of inver sion algorithms are available, there has been limited assessment of performance and no work has been published com paring their accuracy and efficiency. This paper compares the absolute and relative accuracies and computational effi ciencies of one empirical and five radiative-transfer-based published approaches applied to coastal sites at Lee Stocking Island in the Bahamas and Moreton Bay in eastern Australia. These sites have published airborne hyperspectral data and field data. The assessment showed that (1) radiative-transfer‐based methods were more accurate than the empirical approach for bathymetric retrieval, and the accuracies and processing times were inversely related to the complexity of the models used; (2) all inversion methods provided moder ately accurate retrievals of bathymetry, water column inher ent optical properties, and benthic reflectance in waters less than 13 m deep with homogeneous to heterogeneous benth ic/substrate covers; (3) slightly higher accuracy retrievals were obtained from locally parameterized methods; and (4) no method compared here can be considered optimal for all situ ations. The results provide a guide to the conditions where each approach may be used (available image and field data and processing capability). A re-analysis of these same or addi tional sites with satellite hyperspectral data with lower spatial and radiometric resolution, but higher temporal resolution would be instructive to establish guidelines for repeatable regional to global scale shallow water mapping approaches. Optically shallow waters, those where the bottom is visible from the water surface and measurably influences the waterleaving radiance, include inland waters through to estuarine and tropical coral reef and temperate coastal ecosystems. Over

Depletion, Degradation, and Recovery Potential of Estuaries and Coastal Seas

Abstract: Estuarine and coastal transformation is as old as civilization yet has dramatically accelerated over the past 150 to 300 years. Reconstructed time lines, causes, and consequences of change in 12 once diverse and productive estuaries and coastal seas worldwide show similar patterns: Human impacts have depleted >90% of formerly important species, destroyed >65% of seagrass and wetland habitat, degraded water quality, and accelerated species invasions. Twentieth-century conservation efforts achieved partial recovery of upper trophic levels but have so far failed to restore former ecosystem structure and function. Our results provide detailed historical baselines and quantitative targets for ecosystem-based management and marine conservation.

Remote Sensing Image Scene Classification: Benchmark and State of the Art

Abstract: Remote sensing image scene classification plays an important role in a wide range of applications and hence has been receiving remarkable attention. During the past years, significant efforts have been made to develop various datasets or present a variety of approaches for scene classification from remote sensing images. However, a systematic review of the literature concerning datasets and methods for scene classification is still lacking. In addition, almost all existing datasets have a number of limitations, including the small scale of scene classes and the image numbers, the lack of image variations and diversity, and the saturation of accuracy. These limitations severely limit the development of new approaches especially deep learning-based methods. This paper first provides a comprehensive review of the recent progress. Then, we propose a large-scale dataset, termed "NWPU-RESISC45", which is a publicly available benchmark for REmote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU). This dataset contains 31,500 images, covering 45 scene classes with 700 images in each class. The proposed NWPU-RESISC45 (i) is large-scale on the scene classes and the total image number, (ii) holds big variations in translation, spatial resolution, viewpoint, object pose, illumination, background, and occlusion, and (iii) has high within-class diversity and between-class similarity. The creation of this dataset will enable the community to develop and evaluate various data-driven algorithms. Finally, several representative methods are evaluated using the proposed dataset and the results are reported as a useful baseline for future research.

Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios

Abstract: The ocean moderates anthropogenic climate change at the cost of profound alterations of its physics, chemistry, ecology, and services. Here, we evaluate and compare the risks of impacts on marine and coastal ecosystems—and the goods and services they provide—for growing cumulative carbon emissions under two contrasting emissions scenarios. The current emissions trajectory would rapidly and significantly alter many ecosystems and the associated services on which humans heavily depend. A reduced emissions scenario—consistent with the Copenhagen Accord’s goal of a global temperature increase of less than 2°C—is much more favorable to the ocean but still substantially alters important marine ecosystems and associated goods and services. The management options to address ocean impacts narrow as the ocean warms and acidifies. Consequently, any new climate regime that fails to minimize ocean impacts would be incomplete and inadequate.

A survey on object detection in optical remote sensing images

Abstract: Object detection in optical remote sensing images, being a fundamental but challenging problem in the field of aerial and satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. While enormous methods exist, a deep review of the literature concerning generic object detection is still lacking. This paper aims to provide a review of the recent progress in this field. Different from several previously published surveys that focus on a specific object class such as building and road, we concentrate on more generic object categories including, but are not limited to, road, building, tree, vehicle, ship, airport, urban-area. Covering about 270 publications we survey (1) template matching-based object detection methods, (2) knowledge-based object detection methods, (3) object-based image analysis (OBIA)-based object detection methods, (4) machine learning-based object detection methods, and (5) five publicly available datasets and three standard evaluation metrics. We also discuss the challenges of current studies and propose two promising research directions, namely deep learning-based feature representation and weakly supervised learning-based geospatial object detection. It is our hope that this survey will be beneficial for the researchers to have better understanding of this research field.

Advances in oligonucleotide drug delivery

Abstract: Oligonucleotides can be used to modulate gene expression via a range of processes including RNAi, target degradation by RNase H-mediated cleavage, splicing modulation, non-coding RNA inhibition, gene activation and programmed gene editing. As such, these molecules have potential therapeutic applications for myriad indications, with several oligonucleotide drugs recently gaining approval. However, despite recent technological advances, achieving efficient oligonucleotide delivery, particularly to extrahepatic tissues, remains a major translational limitation. Here, we provide an overview of oligonucleotide-based drug platforms, focusing on key approaches - including chemical modification, bioconjugation and the use of nanocarriers - which aim to address the delivery challenge.