N. Estrabis, L. Osco, A. P. Ramos, W. Gonçalves, V. Liesenberg, H. Pistori, J. M. Junior
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引用次数: 1
摘要
Google Earth Engine (GEE)平台是一个在线工具，它可以快速生成图像分类的解决方案，并且不需要本地的高性能计算机。我们研究了2019年11月28日获取的陆地卫星场景(224/076)中大西洋森林地区原生植被和非原生植被的几种数据输入场景。数据输入场景为:I-光谱波段(蓝色至短波红外);II-归一化植被指数NDVI;iii .修正归一化差水指数;IV-情景I和II;V-情景一至情景三。结果表明，利用NDVI和mNDWI组合的光谱波段(场景V)对原生植被制图效果最好，精度为96.64%，kappa指数为0.91。
Brazilian Midwest Native Vegetation Mapping Based on Google Earth Engine
Google Earth Engine (GEE) platform is an online tool, which generates fast solutions in terms of image classification and does not require high performance computers locally. We investigate several data input scenarios for mapping native-vegetation and nonnative-vegetation in the Atlantic Forest region encompassed in a Landsat scene (224/076) acquired on November 28, 2019. The data input scenarios were: I- spectral bands (blue to shortwave infrared); II- NDVI (Normalized Difference Vegetation Index); IIImNDWI (modified Normalized Difference Water Index); IV- scenarios I and II; and V- scenarios I to III. Our results showed that the use of spectral bands added NDVI and mNDWI (scenario V) provided the best performance for the native-vegetation mapping, with accuracy of 96.64% and kappa index of 0.91.
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