Smallholder oil palm plantation sustainability assessment using multi-criteria analysis and unmanned aerial vehicles

Abstract

Oil palm agriculture has caused extensive land cover and land use changes that have adversely affected tropical landscapes and ecosystems. However, monitoring and assessment of oil palm plantation areas to support sustainable management is costly and labour-intensive. This study used an unmanned aerial vehicles (UAV) to map smallholder farms and applied multi-criteria analysis to data generated from orthomosaics, to provide a set of sustainability indicators for the farms. Images were acquired from a UAV, with structure from motion (SfM) photogrammetry then used to produce orthomosaics and digital elevation models of the farm areas. Some of the inherent problems using high spatial resolution imagery for land cover classification were overcome by using texture analysis and geographic object-based image analysis (OBIA). Six spatially explicit environmental metrics were developed using multi-criteria analysis and used to generate sustainability indicator layers from the UAV data. The SfM and OBIA approach provided an accurate, high-resolution (~5 cm) image-based reconstruction of smallholder farm landscapes, with an overall classification accuracy of 89%. The multi-criteria analysis highlighted areas with lower sustainability values, which should be considered targets for adoption of sustainable management practices. The results of this work suggest that UAVs are a cost-effective tool for sustainability assessments of oil palm plantations, but there remains the need to plan surveys and image processing workflows carefully. Future work can build on our proposed approach, including the use of additional and/or alternative indicators developed through consultation with the oil palm industry stakeholders, to support certification schemes such as the Roundtable on Sustainable Palm Oil (RSPO).

Oil Palm Bunch Ripeness Classification and Plantation Verification Platform: Leveraging Deep Learning and Geospatial Analysis and Visualization

“…Consequently, this enables the verification of the accuracy of oil palm plantation plot locations. In contrast, most prior studies have focused solely on satellite image processing, including the use of UAVs [36][37][38][39]. Hence, the development of a platform for classifying and assessing the ripeness of oil palm bunches on trees in this research stands out.…”

“…Additionally, the classification of oil palm plantation areas using Unmanned Aerial Vehicles (UAVs) with a 5 cm image resolution is discussed. This method employs Rule-based Classification to distinguish between vegetation and non-vegetation based on the Normalized Difference Vegetation Index (NDVI), followed by the KNN technique to further classify vegetation types, achieving an 89% accuracy [37]. Furthermore, GIS and RS technologies have been applied to disease detection in oil palms, particularly in the Khlong Thom District of Krabi Province.…”