Region-growing segmentation algorithms are useful for remote sensing image segmentation. These algorithms need the user to supply control parameters, which control the quality of the resulting segmentation. This letter proposes an objective function for selecting suitable parameters for region-growing algorithms to ensure best quality results. It considers that a segmentation has two desirable properties: each of the resulting segments should be internally homogeneous and should be distinguishable from its neighbourhood. The measure combines a spatial autocorrelation indicator that detects separability between regions and a variance indicator that expresses the overall homogeneity of the regions.

Non-technical summaryIt is no longer possible nor desirable to address the dual challenges of equity and sustainability separately. Instead, they require new thinking and approaches which recognize their interlinkages, as well as the multiple perspectives and dimensions involved. We illustrate how equity and sustainability are intertwined, and how a complex social–ecological systems lens brings together advances from across the social and natural sciences to show how (in)equity and (un)sustainability are produced by the interactions and dynamics of coupled social–ecological systems. This should help understand which possible pathways could lead to sustainable and fair futures.

This issue of Current Opinion in Environmental Sustainability provides an overview of recent advances in Land System Science while at the same time setting the research agenda for the Land System Science community. Land System Science is not just representing land system changes as either a driver or a consequence of global environmental change. Land systems also offer solutions to global change through adaptation and mitigation and can play a key role in achieving a sustainable future earth. The special issue assembles 14 articles that entail different perspectives on land systems and their dynamics, synthesizing current knowledge, highlighting currently under-researched topics, exploring scientific frontiers and suggesting ways ahead, integrating a plethora of scientific disciplines.

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Linked Open Science is an approach to solve challenges of an executable paper. It is a combination of four "silver bullets": 1) publication of scientific data, metadata, results, and provenance information using Linked Data principles, 2) open source and web-based environments for executing, validating and exploring research, 3) Cloud Computing for efficient and distributed computing, and 4) Creative Commons for the legal infrastructure. We will use a realistic scientific research setting related to research on deforestation of the Brazilian Amazon rainforest to provide scenarios to illustrate the application of Linked Open Science.

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