Tropical forest

Land degradation

Agriculture

Engineering

Deforestation (computer science)

Ecology

Biology

Normalized Difference Vegetation Index

Time series

Archaeology

Reducing emissions from deforestation and forest degradation

Telecommunications

Civil engineering

Computer science

Geology

Oceanography

Paleontology

Climate change

Forest degradation

Geography

Degradation (telecommunications)

Carbon stock

Hyperspectral imaging

Environmental science

Remote sensing

Series (stratigraphy)

Land use

Programming language

Endmember

Machine learning

Remote Sensing of Environment

Tropical forest loss currently contributes 5 to 15% of anthropogenic carbon emissions to the atmosphere. The large uncertainty in emissions estimates is a consequence of many factors, including differences in definitions of forests and degradation, as well as estimation methodologies. However, a primary factor driving uncertainty is an inability to properly account for forest degradation. While remote sensing offers the only practical way of monitoring forest disturbances over large areas, and despite recent improvements in data quality and quantity and processing techniques, remote sensing approaches are still limited in their ability to detect forest degradation. In this paper, a system is presented that uses time series of Landsat data and spectral mixture analysis to detect both degradation and deforestation in forested landscapes. The Landsat data are transformed into spectral endmember fractions and are used to calculate the Normalized Degradation Fraction Index (NDFI; Souza et al., 2005). The spectrally unmixed data are used for disturbance monitoring and land cover classification via time series analysis. To assess the performance of the system, maps of deforestation and degradation were used to stratify the study area for collection of sample data to which unbiased estimators were applied to produce accuracy and area estimates of degradation and deforestation from 1990 to 2013. The approach extends previous research in spectral mixture analysis for identifying forest degradation to the temporal domain. The method was applied using the Google Earth Engine and tested in the Brazilian State of Rondônia. Degradation and deforestation were mapped with 88.0% and 93.3% User's Accuracy, and 68.1% and 85.3% Producer's Accuracy. Area estimates of degradation and deforestation were produced with margins of error of 13.9% and 5.3%, respectively, over the 24 year time period. These results indicate that for Rondônia a decreasing trend in deforestation after 2004 corresponds to an increase in degradation during the same time period.

Monitoring tropical forest degradation using spectral unmixing and Landsat time series analysis