The utilisation of cloud computing and remote sensing approach to assess environmental sustainability in Malaysia

International Conference on Green Agro-industry and Bioeconomy

农业科学;工业技术（总论）;经济学

Shaharum, N.S.N.^1 ; Shafri, H.Z.M.^1^2 ; K Ghani, W.A.W.A.^3 ; Samsatli, S.^4 ; Yusuf, B.^1

Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia, Selangor, Malaysia^1

Geospatial Information Science Research Centre (GISRC), Faculty of Engineering, Universiti Putra Malaysia, Selangor, Malaysia^2

Department of Chemical and Environmental Engineering, Sustainable Process Engineering Research Centre (SPERC), Faculty of Engineering, Universiti Putra Malaysia, Selangor, Malaysia^3

Department of Chemical Engineering, University of Bath, Claverton Down, United Kingdom^4

关键词: Change analysis;    Conventional methods;    Ecosystem monitoring;    Environmental sustainability;    Land use/ land covers;    Processing time;    Random forests;    Remote sensing approaches;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/230/1/012109/pdf
DOI  :  10.1088/1755-1315/230/1/012109

来源: IOP

【 摘 要 】

Monitoring of the environment over a large area will require huge amount of data. The implementation of conventional methods will be time consuming and very costly. Furthermore, one way to assess the environmental sustainability is by analysing the changes of land cover over two different periods. Therefore, this study implemented a cloud computing approach utilising an open source Remote Ecosystem Monitoring Assessment Pipeline (REMAP) to map the changes of Land use land cover (LULC) over Peninsular Malaysia utilising Landsat data obtained from two different periods (2003 and 2017). This approach has utilised a powerful inbuilt machine learning algorithm, Random Forest (RF) to test the performance of cloud computing using REMAP to produce LULC maps over Peninsular Malaysia. The results showed an acceptable LULC maps and the changes between two periods were analysed. Therefore, the utilisation of machine learning algorithm with the integration of cloud computing using REMAP can reduce the cost, lessen the processing time, produce LULC maps, and perform change analysis over large area.