Review ArticleDigital change detection methods in ecosystem monitoring: a review

By Pol R. Coppin, I. Jonckheere, Kris Nackaerts, B. Muys, Eric F. Lambin. 2004

2,288 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/efb1801e0e1e7fdf1fde8250343ed728cda238b0

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In Atmospheric Science

In Ecology

In Media Technology

Citations in the corpus

Change detection from remotely sensed images: From pixel-based to object-based approaches (2013, methodology): “subtle changes (Coppin et al., 2004) Logistic regression to model”
Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis (2020, background): “Change detection techniques are extensively utilized in various applications [15] including; disaster assessment [16], environmental monitoring [17], land management [18] and urban change analysis [19], etc.”
Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective* (2016, background): “, normalisation through pseudo-invariant features such as barren lands) between the two or more images that represent pre- and post-disturbance stages (Song et al. 2001; Coppin et al. 2004; Lu et al. 2004).”
A critical synthesis of remotely sensed optical image change detection techniques (2015, methodology): “The biggest issue with post-classification 271 change is that it is entirely dependent on the quality of the input maps (Coppin et al., 2004; Lu et al., 2004) 272 with individual errors compounding in the change map (Serra et al., 2003).”
Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology (2013, neutral): “Different states of the land surface can be measured by satellite-derived biophysical parameters [2].”
A review of large area monitoring of land cover change using Landsat data (2012, background): “The most common approach to change detection is bi-temporal change analysis, the direct comparison of pairs of images or characterizations (Coppin et al., 2004).”
Indirect effects of invasive species removal devastate World Heritage Island (2009, background): “These corrections are crucial for change detection algorithms as false changes are often introduced by geometric offsets and shadowing effects (Coppin, Bauer & Marvin 2004).”
Remote Sensing of Mangrove Ecosystems: A Review (2011, background): “, tsunami destruction or conversion to shrimp farms) [104].”
Trajectories of deforestation, coffee expansion and displacement of shifting cultivation in the Central Highlands of Vietnam (2013, positive): “…by combining a post-193 classification comparison reclassified into the main trajectories of land use/cover changes, with an 194 image differencing of the Normalized Difference Vegetation Index (NDVI) with thresholds at +/- 1 195 standard deviation (SD) around the mean (Coppin et al. 2004).”
Using classification and NDVI differencing methods for monitoring sparse vegetation coverage: a case study of saltcedar in Nevada, USA (2008, methodology): “The classification method demonstrates a principal advantage in that the two images acquired at different dates are separately classified in order to minimize the problem of radiometric calibration of images between the different dates (Coppin et al. 2004, Gong and Xu 2003).”