巴西亚马逊地区大地遥感卫星亚像素土地覆被动态变化

IF 2.7 3区 农林科学 Q2 ECOLOGY Frontiers in Forests and Global Change Pub Date : 2023-12-04 DOI:10.3389/ffgc.2023.1294552
Carlos M. Souza, Luis A. Oliveira, Jailson S. de Souza Filho, Bruno G. Ferreira, Antônio V. Fonseca, João V. Siqueira
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引用次数: 0

摘要

由于人为和气候驱动因素,巴西亚马逊地区的土地覆盖迅速变化。森林砍伐和与伐木和火灾相关的森林干扰,加上极端干旱、空气变暖和地表温度,导致该地区树木死亡率高,有害净碳排放量高。本区域土地覆盖动态特征的区域性尝试侧重于一两个人为驱动因素(即毁林和森林退化)。土地覆盖研究也使用有限的时间尺度(即10-15年),主要侧重于全球和国家尺度的森林变化。在这项研究中,我们提出了一种新的方法来表征和测量亚马逊生物群系的土地覆盖动态。首先,我们定义了10个基本的土地覆盖类别:森林、淹水林、灌丛、天然草地、牧场、农田、露头、裸地和不透水地、湿地和水。其次,我们根据陆地卫星亚像元信息的组成丰度绘制了土地覆盖图,这些信息构成了这些土地覆盖类别:绿色植被(GV)、非光合植被、土壤和阴影。第三,我们处理了所有云量<50%的陆地卫星场景。然后,我们应用逐步随机森林机器学习算法和经验决策规则对1985 - 2022年的年度内和年度土地覆盖类别进行分类。最后,我们估算了森林和非森林生态系统土地覆被的年变化,并分析了主要变化驱动因素。2022年,森林覆盖了亚马逊生物群系的78.6% (331.9 Mha),在5年多的时间里,有1.4%的二次再生。草本植物占总面积的15.6%,以牧场为主(13.5%),其余为天然草地。水是第三大土地覆盖类别,占2.4%,其次是农田(1.2%)和灌木林地(0.4%),总体准确率为89%。大部分森林变化是由牧场和农田的转换驱动的,有迹象表明,气候变化是水生生态系统丧失的主要驱动因素。现有的碳排放模型忽略了研究中提出的土地覆盖变化类型。21世纪需要采取更全面和综合的方法来监测亚马逊生物群落的人为和气候变化,以更好地减缓、适应和保护政策。
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Landsat sub-pixel land cover dynamics in the Brazilian Amazon
The Brazilian Amazon land cover changes rapidly due to anthropogenic and climate drivers. Deforestation and forest disturbances associated with logging and fires, combined with extreme droughts, warmer air, and surface temperatures, have led to high tree mortality and harmful net carbon emissions in this region. Regional attempts to characterize land cover dynamics in this region focused on one or two anthropogenic drivers (i.e., deforestation and forest degradation). Land cover studies have also used a limited temporal scale (i.e., 10–15 years), focusing mainly on global and country-scale forest change. In this study, we propose a novel approach to characterize and measure land cover dynamics in the Amazon biome. First, we defined 10 fundamental land cover classes: forest, flooded forest, shrubland, natural grassland, pastureland, cropland, outcrop, bare and impervious, wetland, and water. Second, we mapped the land cover based on the compositional abundance of Landsat sub-pixel information that makes up these land cover classes: green vegetation (GV), non-photosynthetic vegetation, soil, and shade. Third, we processed all Landsat scenes with <50% cloud cover. Then, we applied a step-wise random forest machine learning algorithm and empirical decision rules to classify intra-annual and annual land cover classes between 1985 and 2022. Finally, we estimated the yearly land cover changes in forested and non-forested ecosystems and characterized the major change drivers. In 2022, forest covered 78.6% (331.9 Mha) of the Amazon biome, with 1.4% of secondary regrowth in more than 5 years. Total herbaceous covered 15.6% of the area, with the majority of pastureland (13.5%) and the remaining natural grassland. Water was the third largest land cover class with 2.4%, followed by cropland (1.2%) and shrubland (0.4%), with 89% overall accuracy. Most of the forest changes were driven by pasture and cropland conversion, and there are signs that climate change is the primary driver of the loss of aquatic ecosystems. Existing carbon emission models disregard the types of land cover changes presented in the studies. The twenty first century requires a more encompassing and integrated approach to monitoring anthropogenic and climate changes in the Amazon biome for better mitigation, adaptation, and conservation policies.
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CiteScore
4.50
自引率
6.20%
发文量
256
审稿时长
12 weeks
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