阿塔卡马沙漠生物量指数分类和分析及其与气候变化关系的机器学习

Manglar Pub Date : 2024-04-02 DOI:10.57188/manglar.2024.010
Antos Tito GOMEZ CHOQUEJAHUA, Edwin Martín Pino Vargas, German Huayna Felipe, Jorge Luis Espinoza Molina, Karina Yanina Acosta Caipa, Fredy Cleto Cabrera Olivera
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引用次数: 0

摘要

在这项工作中,我们使用机器学习(兰登森林)工具对生物量进行分类,并计算植被指数,以确定阿塔卡马沙漠头部植被覆盖的特征。目的是建立植被指数与降水量之间的相关性,以了解植被指数对该地区气候的可靠性。基于谷歌地球引擎(GEE)的地理空间分析以及对 Landsat 5 ETM 和 Landsat 8 OLI/TIRS 图像的处理对 1985 - 2022 年期间的气候变化特征描述非常重要。在干旱系统中对 NDVI、SAVI、GVI 和 RVI 进行了测试和验证。归一化差异植被指数对雨季降水量的反应积极,而对冬雨季降水量的反应较弱。经证实,高 NDVI 对应于长期干旱后的夏季。到 2020 年和 2022 年,气温较高的地方植被覆盖率会增加,这证明了气候变化,并反映在生物量指数中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning para la Clasificación y Análisis de los Índices de Biomasa y su relación con el Cambio Climático, Desierto de Atacama
In this work we use Machine Learning (Randon Forest) as a tool to classify biomass and calculate vegetation indices seeking to identify the characteristics of the vegetation cover at the head of the Atacama Desert. The aim is to establish the correlation between vegetation indices and precipitation, in order to know their reliability on the climatology in this region. The geospatial analysis based on Google Earth Engine (GEE) and the processing of Landsat 5 ETM and Landsat 8 OLI/TIRS images was important, for the period 1985 - 2022, which made it possible to characterize climate change. The NDVI, SAVI, GVI and RVI have been tested and validated in arid systems. The NDVI responds positively to precipitation in the wet season and weakly in the winter rainy season. It is confirmed that the high NDVI corresponds to summer, after a prolonged drought. Towards the years 2020 and 2022, an increase in vegetation cover is recorded in places with higher temperatures, evidencing climate change and reflected in biomass indices.
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