Abstract
ABSTRACT Monitoring long-term forest dynamics is essential for assessing human-induced land-cover changes, and related studies are often based on the multi-decadal Landsat archive. However, in areas such as the Tropical Andes, scarce data and the resulting poor signal-to-noise ratio in time series data render the implementation of automated time-series analysis algorithms difficult. The aim of this research was to investigate a novel approach that combines image compositing, multi-sensor data fusion, and postclassification change detection that is applicable in data-scarce regions of the Tropical Andes, exemplified for a case study in Ecuador. We derived biennial deforestation and reforestation patterns for the period from 1992 to 2014, achieving accuracies of 82 ± 3% for deforestation and 71 ± 3% for reforestation mapping. Our research demonstrated that an adapted methodology allowed us to derive the forest dynamics from the Landsat time series, despite the abundant regional data gaps in the archive, namely across the Tropical Andes. This study, therefore, presented a novel methodology in support of monitoring long-term forest dynamics in areas with limited historical data availability.
Highlights
Large-area deforestation assessments indicate that the Amazon Basin lost 13.3% of its forest from 2000 to 2013, where the headwater basins suffered most of the pressure (RAISG, 2015)
They were processed to surface reflectance and acquired from the United States Geological Survey (USGS) Global Archive, sourced through the Earth Resources Observation and Science (EROS) Center Science Processing Architecture (ESPA; USGS, 2014). This data-set included Landsat TM, Enhanced Thematic Mapper plus (ETM+), and the Operational Imager (OLI) data. This ready-to-use data-set is radiometrically calibrated by the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS; Masek et al, 2012) and orthorectified using a digital elevation model (DEM) and ground control points (NASA, 2011)
To the best of our knowledge, this was the first study of its kind that focused on the challenges related to scarce data and the poor signal-to-noise ratio in a long time series for automated forest change analyses in the Tropical Andes
Summary
The Amazon rainforest constitutes one of the biologically most diverse, structurally complex, and carbon-rich bioregions of the world (Asner et al, 2014) It performs essential global-scale functions and provides a multitude of ecosystem services (Paula et al, 2014). Large-area deforestation assessments indicate that the Amazon Basin lost 13.3% of its forest from 2000 to 2013, where the headwater basins suffered most of the pressure (RAISG, 2015). This is alarming for the region itself, as the highland Amazon (or Tropical Andes) is highly susceptible to global warming (Karmalkar, Bradley, & Diaz, 2008), while being under-researched in deforestation studies (Armenteras, Rodríguez, Retana, & Morales, 2011). While lowland tropical forests have been well researched, closing the remaining knowledge gaps on forest dynamics in Andean tropical forests is of prime importance (Armenteras, María, Rodríguez, & Retana, 2017; Da Ponte et al, 2015; Oliveira, Eller, Bittencourt, & Mulligan, 2014; Spracklen & Righelato, 2014)
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