Physical Sciences and Mathematics Commons^™

The Potential Of Remotely Sensed Vegetation Indices For Monitoring Pasture Condition, Pouria Ramzi, Karen Holmes

Resource management technical reports

The Department of Primary Industries and Regional Development (DPIRD) is developing an integrated monitoring system using remote sensing and on-ground measurements to track pasture condition across Western Australia’s pastoral region. We extended and adapted the methods developed in the Pastoral Lease Assessment Using Geospatial Analysis (PLAGA) project (Robinson et al. 2012), which combined remotely sensed vegetation indices (VIs) with on-ground pasture condition observations to assess the potential of using different vegetation indices in a statewide condition monitoring system.

There were 6 regions in WA’s pastoral rangelands with DPIRD on-ground condition traverse points: Kimberley and Broome, Pilbara, Yalgoo and Sandstone, Goldfields, …

Machine Learning For Ecosystem Services, Simon Willcock, Javier Martínez-López, Danny A.P. Hooftman, Kenneth J. Bagstad, Stefano Balbi, Alessia Marzo, Carlo Prato, Saverio Sciandrello, Giovanni Signorello

Rubenstein School of Environment and Natural Resources Faculty Publications

Recent developments in machine learning have expanded data-driven modelling (DDM) capabilities, allowing artificial intelligence to infer the behaviour of a system by computing and exploiting correlations between observed variables within it. Machine learning algorithms may enable the use of increasingly available ‘big data’ and assist applying ecosystem service models across scales, analysing and predicting the flows of these services to disaggregated beneficiaries. We use the Weka and ARIES software to produce two examples of DDM: firewood use in South Africa and biodiversity value in Sicily, respectively. Our South African example demonstrates that DDM (64–91% accuracy) can identify the areas where …