Mapping of oil palm land cover using integration of cloud computing, machine learning and big data
Oil palm is one of the agricultural crops that produces high amount of biomass, in which contributes to support the Sustainable Development Goals (SDGs). Furthermore, oil palm is a climate-friendly product that can generate energy in a more efficient way than using harmful eleme...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2019
|
| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/84394/1/FK%202019%20145%20-%20ir.pdf http://psasir.upm.edu.my/id/eprint/84394/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Oil palm is one of the agricultural crops that produces high amount of biomass, in
which contributes to support the Sustainable Development Goals (SDGs). Furthermore,
oil palm is a climate-friendly product that can generate energy in a more efficient way than using
harmful element such as fossil fuel. However, the expansion of oil palm plantation has been
recognised as a threat to the wildlife species and had caused massive amount of deforestations.
A tropical country with humid weather, Malaysia has been listed as one of the top three
countries with the highest rate of deforestation in the world. Obtaining information of oil palm
plantation over a large area will be very intensive, costly and time consuming. Thus, this study
had utilised cloud-based platforms, namely Google Earth Engine (GEE) and Remote Ecosystem
Monitoring Assessment Pipeline (REMAP) to map the oil palm areas. Random Forest (RF) machine
learning algorithm was utilised to produce and classify the land cover maps covering the Peninsular
Malaysia. By using Landsat data obtained in Period 1 (1999 – 2003) and Period 2 (2014 – 2017), both
cloud-based platforms were able to produce the land cover maps of Peninsular Malaysia. The overall
accuracies produced by the GEE and REMAP for Period 1 data were 78.60% and 79.52% respectively.
Meanwhile, the overall accuracies produced for Period 2 data were 79.77% and 80.00% for
GEE and REMAP respectively. The changes of oil palm plantations noted from Period 1 to Period 2
using both cloud-based platforms were assessed, and the results showed oil palm plantation in
Peninsular Malaysia is at sustainable state. For the first time, cloud-based platforms such as
REMAP and GEE were being assessed for monitoring the changes to oil palm land cover
in Peninsular Malaysia. Furthermore, the utilisation of REMAP and GEE were implemented to
validate each other and to see the consistency of the results produced. This is a new paradigm
shift from the normal approach utilising desktop-based big archives data analysis over huge areas
that consumes tremendous amount of time and computing resources. In conclusion, both GEE
and REMAP were able to produce the oil palm land cover maps and the changes of the oil palm can be
analysed. In the future, the produced oil palm land cover maps of Peninsular Malaysia can be integrated with other ancillary data in
a Geographic Information System (GIS) which later can assist the authorities in
producing better decision-making and action plans. |
|---|