Forest Disturbance Detection with Seasonal and Trend Model Components and Machine Learning Algorithms
Por:
Solorzano, Jonathan V., Gao, Yan
Publicada:
1 feb 2022
Categoría:
Earth and planetary sciences (miscellaneous)
Resumen:
Forest disturbances reduce the extent of natural habitats, biodiversity,
and carbon sequestered in forests. With the implementation of the
international framework Reduce Emissions from Deforestation and forest
Degradation (REDD+), it is important to improve the accuracy in the
estimation of the extent of forest disturbances. Time series analyses,
such as Breaks for Additive Season and Trend (BFAST), have been
frequently used to map tropical forest disturbances with promising
results. Previous studies suggest that in addition to magnitude of
change, disturbance accuracy could be enhanced by using other components
of BFAST that describe additional aspects of the model, such as its
goodness-of-fit, NDVI seasonal variation, temporal trend, historical
length of observations and data quality, as well as by using separate
thresholds for distinct forest types. The objective of this study is to
determine if the BFAST algorithm can benefit from using these model
components in a supervised scheme to improve the accuracy to detect
forest disturbance. A random forests and support vector machines
algorithms were trained and verified using 238 points in three different
datasets: all-forest, tropical dry forest, and temperate forest. The
results show that the highest accuracy was achieved by the support
vector machines algorithm using the all-forest dataset. Although the
increase in accuracy of the latter model vs. a magnitude threshold model
is small, i.e., 0.14% for sample-based accuracy and 0.71% for
area-weighted accuracy, the standard error of the estimated total
disturbed forest area was 4352.59 ha smaller, while the annual
disturbance rate was also smaller by 1262.2 ha year(-1). The implemented
approach can be useful to obtain more precise estimates in forest
disturbance, as well as its associated carbon emissions.
Filiaciones:
Solorzano, Jonathan V.:
Univ Nacl Autonoma Mexico, Ctr Invest Geog Ambiental, Posgrad Geog, Morelia 58190, Michoacan, Mexico
Gao, Yan:
Univ Nacl Autonoma Mexico, Ctr Invest Geog Ambiental, Morelia 58190, Michoacan, Mexico
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