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首页> 外文会议>Asian conference on remote sensing;ACRS >IDENTIFICATION OF SMALLHOLDERS' OIL PALM PLANTATIONS USING ALOS DATA (A CASE STUDY IN LAMPUNG PROVINCE, INDONESIA)
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IDENTIFICATION OF SMALLHOLDERS' OIL PALM PLANTATIONS USING ALOS DATA (A CASE STUDY IN LAMPUNG PROVINCE, INDONESIA)

机译：利用ALOS数据识别小棕榈油植物园（以印度尼西亚南邦省为例）

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Oil palm is one of the most profitable crops for developing countries in tropical area. The expansion of oil palm cultivation has been rapidly increased to meet growing demands of palm oil, but it is also considered as a great threat for tropical ecosystems. Sustainable monitoring, management, and environmental effect assessment of this expansion is important to lower its contribution to biodiversity loss and climate change, while also fostering vigorous national economic development. As one of the efforts, accurate identification of all management types of oil palm plantations is necessary. Remote sensing technology is known to have high potential to identify the spatial distribution of any land cover types. However, the identification of smallholders' oil palm plantations is a great challenge since its sparse distribution in small patches is still practically hard to be identified using currently available remote sensing data. The present study aimed to assess the ability of textural analysis to detect oil palms and to identify the smallholders' oil palm plantations in Mesuji District, Lampung Province, Indonesia by using both of microwave and optical images of the Advanced Land Observing Satellite (ALOS). Texture analysis using eight texture features in six types moving windows was examined to extract the triangular planting pattern from ALOS Phased Array type L-Band Synthetic Aperture Radar (PALSAR) dual polarimetry data. The results showed mean and variance features in 11 x 11 moving window as the best combination for identifying oil palms. An improvement in the classification accuracy was obtained through the integration of the texture analysis result with ALOS Advanced Visible and Near Infrared Radiometer type-2 (AVNIR-2) image. This method also revealed a great possibility to differentiate between the young and mature oil palms.
机译：油棕是热带地区发展中国家最赚钱的作物之一。为了满足日益增长的棕榈油需求，油棕种植的规模已迅速增加，但也被认为是对热带生态系统的巨大威胁。对这一扩展进行可持续的监测，管理和环境影响评估，对于降低其对生物多样性丧失和气候变化的贡献非常重要，同时也可以促进国家经济的蓬勃发展。作为一项工作，必须准确识别油棕种植园的所有管理类型。众所周知，遥感技术具有识别任何土地覆盖类型的空间分布的巨大潜力。但是，确定小农的油棕种植园是一个巨大的挑战，因为实际上很难利用目前可获得的遥感数据来确定小农户在小片中的稀疏分布。本研究旨在通过使用高级陆地观测卫星（ALOS）的微波和光学图像评估质地分析检测油棕的能力，并确定印度尼西亚楠榜省Mesuji区小农油棕种植园的能力。在六个类型的移动窗口中使用八个纹理特征进行了纹理分析，以从ALOS相控阵L型波段合成孔径雷达（PALSAR）双极化数据中提取三角形种植图案。结果表明，在11 x 11移动窗口中，均值和方差特征是识别油棕的最佳组合。通过将纹理分析结果与ALOS 2型高级可见和近红外辐射计（AVNIR-2）图像相集成，可以提高分类精度。该方法还揭示了区分年轻和成熟油棕的巨大可能性。

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