IIIT Hyderabad Publications |
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A Shape-based Approach to Spatio-Temporal Data Analysis using Satellite ImageryAuthor: Darpan Baheti Date: 2018-11-21 Report no: IIIT/TH/2018/78 Advisor:K S Rajan AbstractMany socio-environmental aspects manifest themselves over space and time, interacting at varying scales of these dimensions. Satellite imagery, available repetitively over a region, provide important clues of these observations across these dimensions. But, also pose enormous challenges in terms of data processing, extracting significant patterns (indicating the underlying processes) and be able to fur- ther model them as scientific knowledge of the environmental process. Study of cropping practices and changes occurring in it over time for a region is critical to the field of agronomics. The increase of temporal resolution of Earth Observation Satellite platforms has re- sulted in easier monitoring of the Earth’s surfaces. Satellite imagery is an exemplary data source that captures the vegetation responses of the field and such dataset can be used to effectively monitor and track vegetation health over time. The need for large-scale and structured analysis of satellite data for agricultural monitoring is growing. One of the current challenges in understanding agricultural prac- tices is to build a history of land-use outcomes and extracting the local phenological responses that help in building field-based inventory and understanding gross estimate of various cropping systems. Also, monitoring of vegetation health and identification of the major changes in crop practice described here as change-event can provide valuable insights into the possible factors that were responsible for such an event be it biological, physical, hydrological or climatic. In this study, an effort has been made to propose a time-variant analysis method based on the shape characteristics of the vegetation response over time to help identify regions of significant changes. This study attempts to build a spatio-temporal library of vegetation practices and presents an approach, supervised and unsupervised, to process the raw data obtained from the satellite images into representative phenological growth curves. The statis- tics contributed by the current work is critical to understanding and assessing the changes in cropping practices over time and detecting possible drivers that triggered to cause such change-events. The study covers four agricultural-year periods between 2008 and 2012 over the district of West Godavari, in the south of India. An interesting result from this study was to observe the impact of drought on cropping cycles during and after the drought year for the given region. The work finds that the immediate effects of drought in a given year are limited if the region is well endowed with other resources such as irrigation facility etc. Whereas its impacts on cropping patterns can also be seen in the season following the drought year. And such changes vary across the district depending on where vi vii cropping is spatially located. The approach uncovers that the effect of the 2009 drought year on the agricultural practices vary spatially depending on the access to resources and the time-lag that manifests itself in such processes. In this study, we also find that nearly 80% of the region is well endowed and hence resilient to the climatic vagaries. The major contribution of this work is the analysis of spatio-temporal data to detect changes that may help in capturing episodic and periodic change-events occurring in crop cycles of any given region. With vast data spanning across multiple regions in hand, the proposed shape-based approach that characterize the phenological nature of these time-dependent sequences help to discover regions that are spatially correlated and find crop practices that are prevalent across similarly endowed regions. Full thesis: pdf Centre for Spatial Informatics |
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