Remote sensing change detection tools for natural resource managers: Understanding concepts and tradeoffs in the design of landscape monitoring projects

By Robert E. Kennedy, Philip A. Townsend, J. Gross, Warren B. Cohen, P. Bolstad, Yeqiao Wang, P. Adams. 2009

435 citations recorded by the source corpus.

Remote sensing provides a broad view of landscapes and can be consistent through time, making it an important tool for monitoring and managing protected areas. An impediment to broader use of remote sensing science for monitoring has been the need for resource managers to understand the specialized capabilities of an ever-expanding array of image sources and analysis techniques. Here, we provide guidelines that will enable land managers to more effectively collaborate with remote sensing scientists to develop and apply remote sensing science to achieve monitoring objectives. We first describe fundamental characteristics of remotely sensed data and change detection analysis that affect the types and range of phenomena that can be tracked. Using that background, we describe four general steps in natural resource remote sensing projects: image and reference data acquisition, pre-processing, analysis, and evaluation. We emphasize the practical considerations that arise in each of these steps. We articulate a four-phase process that guides natural resource and remote sensing specialists through a collaborative process to articulate goals, evaluate data and options for image processing, refine or eliminate unrealistic paths, and assess the cost and utility of different options.

Subfields: Ecological Modeling, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/ce207ed01d02ea446e643d0ac81be090328931da

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Citations in the corpus

Change detection from remotely sensed images: From pixel-based to object-based approaches (2013, background): “…representation than a conventional ‘hard’ classification Labeling change among a matrix of many overlapping classes may be difficult or non-informative (Kennedy et al., 2009) Topographic map revision (Metternicht, 1999) Unsupervised change detection (Bruzzone and Prieto, 2000) Landscape change…”
Opening the archive: How free data has enabled the science and monitoring promise of Landsat (2012, background): “understanding ecological and land cover dynamics (Kennedy et al., 2009).”
A survival guide to Landsat preprocessing. (2017, methodology): “However, each preparation step further alters the data from their original values, increasing the potential to introduce error (Kennedy et al. 2009).”
Big data for forecasting the impacts of global change on plant communities (2017, background): “Frameworks (Phinn et al. , 2003) and guidelines (Kennedy et al. , 2009) for systematically identifying remotely sensed and other geospatial data and processing methods to address a particular problem can improve the performance of environmental models (Bennett et al. , 2013).”