Humid tropical forest disturbance alerts using Landsat data
By Matthew C. Hansen, Alexander Krylov, Alexandra Tyukavina, Peter V. Potapov, Svetlana Turubanova, Brian R Zutta, S. Ifo, B. Margono, F. Stolle, R. Moore. 2016
281 citations recorded by the source corpus.
A Landsat-based humid tropical forest disturbance alert was implemented for Peru, the Republic of Congo and Kalimantan, Indonesia. Alerts were mapped on a weekly basis as new terrain-corrected Landsat 7 and 8 images were made available; results are presented for all of 2014 and through September 2015. The three study areas represent different stages of the forest land use transition, with all featuring a variety of disturbance dynamics including logging, smallholder agriculture, and agroindustrial development. Results for Peru were formally validated and alerts found to have very high user’s accuracies and moderately high producer’s accuracies, indicating an appropriately conservative product suitable for supporting land management and enforcement activities. Complete pan-tropical coverage will be implemented during 2016 in support of the Global Forest Watch initiative. To date, Global Forest Watch produces annual global forest loss area estimates using a comparatively richer set of Landsat inputs. The alert product is presented as an interim update of forest disturbance events between comprehensive annual updates. Results from this study are available for viewing and download at http://glad.geog.umd.edu/forest-alerts and www.globalforestwatch.org.
Subfields: Ecology, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/09261485e6ad4fe6334cb21163fddacad4ca4239
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Ecology
In Global and Planetary Change
Citations in the corpus
- Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications (2017, methodology): “…et al., 2010; Gavier-Pizarro et al., 2012; Hayes and Sader, 2001; Sader et al., 2003; Sader and Legaard, 2008; Wilson and Sader, 2002) and high (Hansen et al., 2016, 2014, 2013; Hansen and Loveland, 2012; Potapov et al., 2011, 2015, 2012) frequencies of Landsat time series, while only a few…”
- Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar (2023, background): “Of the 1,450 initial plots considered, we removed 291 that had tree cover loss since 2014 in the dataset of Hansen et al. (2016).”