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Spatiotemporal analysis of long-term vegetation changes in New Zealand

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conference contribution
posted on 2024-08-13, 21:10 authored by Duy TranDuy Tran, Estelle DominatiEstelle Dominati, Rachel ColeRachel Cole, Diane Pearson, Thuong V. Tran, Soe W. Myint, David Bruce, John LowryJohn Lowry

Conclusions

  • Spatiotemporal analysis methods such as MK-TS, time-series hot spot analysis, and time-series clustering provide more meaningful and informative results.
  • The spatiotemporal statistical approach applied to remotely time-series data can be used to solve complex questions: quantifying the trend, pattern, and rate of vegetation dynamics; examining the spatial relationship between vegetation and environmental changes; predicting changes associated with environmental and climate change
  • Modern geospatial technologies (time-series big data, advanced spatial statistical analytics) will provide better management options for a sustainable future land use.

History

Publication date

2024-04-08

Project number

  • Non revenue

Language

  • English

Does this contain Māori information or data?

  • No

Publisher

AgResearch Ltd

Conference name

International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS)

Conference location

Wellington, New Zealand

Conference start date

2024-04-08

Conference end date

2024-04-10

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