利用谷歌地球引擎在社会森林计划中监测农业森林结构

Majalah Geografi Indonesia Pub Date : 2022-09-21 DOI:10.22146/mgi.73923

Ahmad Rizaldi, A. Darmawan, Hari Kaskoyo, Agus Setiawan

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引用次数: 0

摘要

摘要社会林业计划中的森林管理战略-农林需要使用遥感技术进行监测。遥感成像分析和信息技术现在已经发展成为云计算和大数据的使用，例如谷歌地球引擎（GEE）平台，它可以非常快速地下载土地覆盖等卫星图像数据。因此，本文旨在利用GEE平台，结合随机森林（RF）算法和分类回归树（CART），在社会森林监测的背景下分析多时间卫星图像。分类准确度测试结果表明，与获得89.77%的总准确度值和85.54%的kappa准确度值的CART算法相比，RF算法具有更好的准确度结果，总准确度为94.64%，kappa准确率为92.23%。事实证明，使用谷歌地球引擎平台监测PS计划在某些领域成功地缓解了森林砍伐和退化的加剧。摘要农林森林管理需要利用遥感技术进行和监测。今天与遥感技术相关的最新发展是使用云计算和谷歌地球引擎（GEE）等大数据，这使得从卫星图像（如土地覆盖）中获取衍生数据变得非常快。本文旨在利用GEE与随机森林（RF）和分类与回归树（CART）算法分析多时间卫星图像。结果表明，与CART算法相比，RF算法具有更好的分类准确率，总体准确率为94.64%，kappa准确率为92.23%，CART算法的总体准确率值为89.77%，kappa准确性值为85.54%森林砍伐和森林退化。

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Pemanfaatan google earth engine untuk pemantauan lahan agroforestri dalam skema perhutanan sosial

Abstrak. Strategi pengelolaan hutan secara agroforestri dalam skema Perhutanan Sosial (PS) perlu dipantau menggunakan teknologi penginderaan jauh. Teknologi analisis citra penginderaan jauh dan teknologi informasi saat ini telah berkembang ke dalam penggunaan cloud computing dan Big Data seperti platform Google Earth Engine (GEE) yang membuat perolehan data turunan citra satelit seperti tutupan lahan menjadi sangat cepat. Makalah ini bertujuan untuk menganalisis citra satelit multiwaktu menggunakan platform GEE dengan algoritma Random Forest (RF) dan Classification and Regression Trees (CART) dalam konteks pemantauan program perhutanan sosial. Hasil uji penilaian akurasi klasifikasi menunjukkan bahwa algoritma RF memiliki hasil akurasi lebih baik dengan nilai overall accuracy sebesar 94,64% dan kappa accuracy sebesar 92,23% dibandingkan dengan algoritma CART yang mendapatkan nilai overall accuracy sebesar 89,77% dan nilai kappa accuracy sebesar 85,54%. Penggunaan platform google earth engine untuk pemantauan skema PS terbukti berhasil di beberapa daerah dalam penerapan mitigasi peningkatan deforestasi dan degradasi hutan. Abstract. Agroforestry forest management needs to be carried out and monitored using remote sensing technology. The latest development related to remote sensing technology today is using cloud computing and Big Data such as the Google Earth Engine (GEE), which makes the acquisition of derived data from satellite imagery such as land cover very quickly. This paper aims to analyze multi-time satellite imagery using GEE with Random Forest (RF) and Classification and Regression Trees (CART) algorithms. The results show that the RF algorithm has better classification accuracy with an overall accuracy value of 94.64% and kappa accuracy of 92.23% compared to the CART algorithm which gets an overall accuracy value of 89.77% and kappa accuracy value of 85.54%. The use of GEE platform for monitoring PS schemes has proven successful in several areas in implementing mitigation of increased deforestation and forest degradation.

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Majalah Geografi Indonesia

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审稿时长

22 weeks