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SIIA Público
SISTEMA INTEGRAL DE INFORMACIÓN ACADÉMICA - PÚBLICO
Título del libro: 2020 Ieee Latin American Grss & Isprs Remote Sensing Conference (lagirs) Título del capítulo: FOREST DISTURBANCE DETECTION BY LANDSAT-BASED NDVI TIME SERIES FOR AYUQUILA RIVER BASIN, JALISCO, MEXICO
Autores UNAM: YAN GAO;
Autores externos: Idioma: Año de publicación: 2020Palabras clave:
Deforestation; Forest degradation; Magnitude of change
Resumen:
Time series data have been applied for forest disturbance detection. The
validation of detected changes is challenging partially because the
validation data are often not readily available. Unlike multi-temporal
change analysis, time series analysis not only detects areas of change
but also reports time of change. Both spatial and temporal accuracy are
therefore important for the accuracy assessment. Ayuquila River Basin
(ARB) is one of the early action areas in Mexico for the implementation
of REDD + initiatives under UNFCCC. In ARB, shifting cultivation and
cattle grazing often take place, resulting in degraded forestland.
Sub-annual forest disturbance detection and estimation contribution to
the improved local forest management and REDD + implementation.
Landsat-based NDVI time series data covering 1999-2018 were analysed
using linear regression and the breakpoints of change and the magnitude
of change were detected. Breakpoints with magnitude of change ranging
from (-0.05) to (-0.2) were verified during a field campaign in October
2018. Here the magnitude of change is related with NDVI differences.
Areas with magnitude of change higher than (-0.2) were identified as
false changes. Verification data were generated by visually interpreting
time series Landsat images of 2016-2018. In this way, areas with forest
loss were identified. By stratified random sampling, 683 points were
applied for the verification including 511 points of forests and 172
points of forest loss. It yields 75.84% for the overall accuracy of the
change detection; for the detected forest loss as a category, the user's
accuracy is 88.89% and the producer's accuracy is 0.46%. A possible
reason for the very low producer's accuracy is that the selected
magnitude value (-0.2) is too low and some of the detected changes were
filtered out.