Abstract

A fast, precise and efficient method of savannah grassland mapping and monitoring is essential to support sustainable livestock feed management. This study aims to utilise Sentinel-2A Level-1C imagery to map and monitor tropical savannah grasslands on Sabu Island, Indonesia. Normalized Difference Vegetation Index (NDVI) images were generated to identify vegetation objects from 50 image scenes covering each month from 2016 to 2020 through the Google Earth Engine (GEE). Principal Component Analysis (PCA) was applied to the 50 NDVI data to produce monthly images (12 months). The grassland objects were classified from Sentinel-2A images using the parallelepiped algorithm and resulted in an overall accuracy of 82.86%. Results showed a range of the average monthly NDVI between 0.127 and 0.449, which falls within the grassland class. NDVI combined with GEE can quickly and accurately identify grasslands, creating highly recommended tools for monitoring tropical savannah grasslands.

摘要

一种快速、精确和有效的萨凡纳草原测绘和监测方法对于支持可持续的牲畜饲料管理至关重要。本研究旨在利用 Sentinel-2A Level-1C 图像来绘制和监测印度尼西亚萨布岛的热带稀树草原草原。生成归一化差异植被指数 (NDVI) 图像，以通过谷歌地球引擎 (GEE) 从 2016 年至 2020 年每月覆盖的 50 个图像场景中识别植被对象。将主成分分析 (PCA) 应用于 50 个 NDVI 数据以生成月度图像（12 个月）。使用平行六面体算法从 Sentinel-2A 图像中对草地对象进行分类，总体准确率为 82.86%。结果显示，月平均 NDVI 范围在 0.127 到 0.449 之间，属于草原类。