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Object-based Dimensionality Reduction in Land Surface Phenology Classification

1 Environmental Systems Research Institute (Esri), 3060 Little Hills Expressway, St. Charles, MO, USA
2 Department of Geosciences and Center for Advanced Spatial Technologies (CAST), University of Arkansas, Fayetteville, AR, USA
3 Dartmouth College, Hanover, NH, USA

Special Issues: Special Issue on Satellite Remote Sensing

Unsupervised classification or clustering of multi-decadal land surface phenology provides a spatio-temporal synopsis of natural and agricultural vegetation response to environmental variability and anthropogenic activities. Notwithstanding the detailed temporal information available in calibrated bi-monthly normalized difference vegetation index (NDVI) and comparable time series, typical pre-classification workflows average a pixel’s bi-monthly index within the larger multi-decadal time series. While this process is one practical way to reduce the dimensionality of time series with many hundreds of image epochs, it effectively dampens temporal variation from both intra and inter-annual observations related to land surface phenology. Through a novel application of object-based segmentation aimed at spatial (not temporal) dimensionality reduction, all 294 image epochs from a Moderate Resolution Imaging Spectroradiometer (MODIS) bi-monthly NDVI time series covering the northern Fertile Crescent were retained (in homogenous landscape units) as unsupervised classification inputs. Given the inherent challenges of in situ or manual image interpretation of land surface phenology classes, a cluster validation approach based on transformed divergence enabled comparison between traditional and novel techniques. Improved intra-annual contrast was clearly manifest in rain-fed agriculture and inter-annual trajectories showed increased cluster cohesion, reducing the overall number of classes identified in the Fertile Crescent study area from 24 to 10. Given careful segmentation parameters, this spatial dimensionality reduction technique augments the value of unsupervised learning to generate homogeneous land surface phenology units. By combining recent scalable computational approaches to image segmentation, future work can pursue new global land surface phenology products based on the high temporal resolution signatures of vegetation index time series.
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