Improving Neural Network classification of native forest in New Zealand with phenological features

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Journal Article
Thesis discipline
Degree name
Informa UK Limited
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Ye, Ning
morgenroth, justin
Xu, Cong

Changes in Vegetation Indices (VIs) over time can describe vegetation phenology; however, it is not known which phenological features contribute most to land cover classification. Feature selection could potentially solve this problem. In this study, phenological feature importance and selection were evaluated by using two-year Sentinel-2 (S-2) data and single-date PlanetScope (PS) to classify a 50 km2 native podocarp forest in New Zealand. The study area was classified into nine classes. Single-date PS and S-2 data were fused to a base image with the same spatial resolution as PS and 8 bands containing spectral data from S-2; this image was used to produce 30 Vegetation Indices (VIs). Phenological features–amplitude (AMP) and phase (PH) were extracted from these VIs using time-series S-2 only, and harmonic analysis in Google Earth Engine. For accurately classifying forests and identifying the most important features, three classification scenarios (fused bands & VIs, fused bands & phenological features, fused bands & VIs & phenological features) were developed using a Neural Network. Variable Selection Using Random Forest (VSURF) was applied on these scenarios to evaluate the impact of feature selection. Results indicate that VSURF could reduce the time needed for the classification while maintaining a comparable level of accuracy. Phenological features improved accuracy from 90% to 94%, driven mostly by Red-Edge Triangulated Vegetation Index-AMP&PH, Normalised Near-Infrared-PH, Greenness Index-PH, Water Body Index-PH, Normalised Difference Vegetation Index-PH, Normalised Green-PH, Red-Edge Normalised Difference Vegetation Index-PH, Leaf Chlorophyll Content-AMP, and Simple Near-Infrared and Blue Ratio-PH. These features reflect changes in the structure, biochemical, and physiological characteristics of the canopy. A lack of ground-based measurements precluded an evaluation of the accuracy of these phenological aspects and an explanation of their distinctive contribution to the model. Overall, the findings show that specific phenological features can improve the classification of New Zealand’s indigenous podocarp forests.

Ye N, Morgenroth J, Xu C (2023). Improving Neural Network classification of native forest in New Zealand with phenological features. International Journal of Remote Sensing. 44(19). 6147-6166.
phenology, vegetation classification, machine learning, time-series data, Google Earth Engine
Ngā upoko tukutuku/Māori subject headings
ANZSRC fields of research
30 - Agricultural, veterinary and food sciences::3007 - Forestry sciences::300707 - Forestry management and environment
40 - Engineering::4013 - Geomatic engineering::401305 - Satellite-based positioning
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