Techniques based on multi-temporal, multi-spectral, satellite-sensor-acquired data have demonstrated potential as a means to detect, identify, map and monitor ecosystem changes, irrespective of their causal agents. This review paper, which summarizes the methods and the results of digital change detection in the optical/infrared domain, has as its primary objective a synthesis of the state of the art today. It approaches digital change detection from three angles. First, the different perspectives from which the variability in ecosystems and the change events have been dealt with are summarized. Change detection between pairs of images (bi-temporal) as well as between time profiles of imagery derived indicators (temporal trajectories), and, where relevant, the appropriate choices for digital imagery acquisition timing and change interval length definition, are discussed. Second, pre-processing routines either to establish a more direct linkage between remote sensing data and biophysical phenomena, or to temporally mosaic imagery and extract time profiles, are reviewed. Third, the actual change detection methods themselves are categorized in an analytical framework and critically evaluated. Ultimately, the paper highlights how some of these methodological aspects are being fine-tuned as this review is being written, and we summarize the new developments that can be expected in the near future. The review highlights the high complementarity between different change detection methods.

/pdf/digital-change-detection-methods-in-ecosystem-monitoring-a-3qdpdqn88d.pdf

Digital change detection methods in ecosystem monitoring: a review

Object-based cloud and cloud shadow detection in Landsat imagery

https://www.fs.fed.us/pnw/pubs/journals/pnw_2010_kennedy002.pdf

Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms

An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks

Detection of forest harvest type using multiple dates of Landsat TM imagery

Two variations of image differencing were compared. The first was based on unsupervised classification, repeated five times, using five sequential date-pairs of difference images between 1972 and 1993. Referred to as merged image differencing, this method required merging the results from five separate time intervals into a single map of forest harvest activity. The other method involved a single unsupervised classification of the full sequential difference image data set, and was referred to as simultaneous image differencing. A thorough harvest map error assessment using an independent reference database was compared to two methods of assessment based on visual interpretation of the Landsat data used to develop the difference images. Results indicate that harvest activity was mapped using merged image differencing with greater than 90 percent accuracy, and that visual methods of error assessment using the Landsat images gave nearly identical results with those of the independent reference data. Simultaneous image differencing resulted in a map that was consistent with merged image differencing, and was considerably more cost-effective to implement.

http://faculty.wwu.edu/wallin/envr442/pdf_files/cohen_etal_1998_apr_293-300.pdf

An efficient and accurate method for mapping forest clearcuts in the Pacific Northwest using Landsat imagery

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An efficient and accurate method for mapping forest clearcuts in the Pacific Northwest using Landsat imagery