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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).
摘要：
油棕农业造成了广泛的土地覆盖和土地利用变化，对热带景观和生态系统产生了不利影响。然而,监测和评估油棕种植区以支持可持续管理是昂贵和劳动密集型的。这项研究使用无人驾驶飞行器（UAV）来绘制小农农场的地图，并将多准则分析应用于从正交测量产生的数据，为农场提供一套可持续性指标。图像是从无人机上获取的，具有运动结构（SfM）摄影测量法，然后用于产生农场区域的正交和数字高程模型。通过使用纹理分析和基于地理对象的图像分析（OBIA）克服了使用高空间分辨率图像进行土地覆盖分类的一些固有问题。使用多准则分析开发了六个空间显式环境指标，并用于从无人机数据生成可持续性指标层。SfM和OBIA方法提供了一种准确的，基于高分辨率（〜5厘米）图像的小农农场景观重建，总体分类准确率为89%。多标准分析突出了可持续性价值较低的地区，应将其视为采用可持续管理实践的目标。这项工作的结果表明，无人机是油棕种植园可持续性评估的具有成本效益的工具，但是仍然需要仔细规划调查和图像处理工作流程。未来的工作可以基于我们提出的方法，包括使用通过与油棕行业利益相关者协商制定的额外和/或替代指标，支持认证计划，如可持续棕榈油圆桌会议(RSPO)。
