Palm oil is the highest yielding oil crop of the world. Oil palms are extensively planted in the equator zone, especially in Indonesia where plantations have been spreading in response of the increasing market demand. Cultivation of oil palm in tropical countries it has already proved of endangering biodiversity and degrading environment with a global impact related to forest loss. From this point of view, remote sensing can support a more efficient plantation management that takes into account its effects over environment. In this work a time series of EVI (Enhanced Vegetation Index) maps, covering the period 2000–2018, was generated from the MODIS Vegetation Index product (MOD13Q1-v5) with the aim of automatically detecting new oil palm plantations and possibly giving an estimate of their age. To achieve these goals a self-developed routine was implemented to automatically operate the EVI time series analysis. Accuracy assessment showed an overall accuracy in new palm oil plantations detection of about 94%. Starting age estimation proved to be accurate enough: 76% of estimates were placed in a time range of 1 year in respect of the actual plantation date, as communicated by farmers. The method was thought to be reliable and simple enough to be easily engineered in a web based geo service useful for the main players of the sector.

Detection and Characterization of Palm Oil Plantations through MODIS EVI time series

De Petris, S.;Borgogno Mondino, E.
2018

Abstract

Palm oil is the highest yielding oil crop of the world. Oil palms are extensively planted in the equator zone, especially in Indonesia where plantations have been spreading in response of the increasing market demand. Cultivation of oil palm in tropical countries it has already proved of endangering biodiversity and degrading environment with a global impact related to forest loss. From this point of view, remote sensing can support a more efficient plantation management that takes into account its effects over environment. In this work a time series of EVI (Enhanced Vegetation Index) maps, covering the period 2000–2018, was generated from the MODIS Vegetation Index product (MOD13Q1-v5) with the aim of automatically detecting new oil palm plantations and possibly giving an estimate of their age. To achieve these goals a self-developed routine was implemented to automatically operate the EVI time series analysis. Accuracy assessment showed an overall accuracy in new palm oil plantations detection of about 94%. Starting age estimation proved to be accurate enough: 76% of estimates were placed in a time range of 1 year in respect of the actual plantation date, as communicated by farmers. The method was thought to be reliable and simple enough to be easily engineered in a web based geo service useful for the main players of the sector.
AIT2018: THE IX CONFERENCE OF THE ITALIAN SOCIETY OF REMOTE SENSING
Firenze (Italy)
4-6/7/2018
FROM SPACE TO LAND MANAGEMENT remote sensing technologies supporting sustainable development and natural resource management
AIT
77
79
MODIS EVI, Time series, Oil palm, Palm age, Palm detection, Borneo
De Petris, S.; Boccardo, P.; Borgogno Mondino, E.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/1679698
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