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利用卷积网络,使用指示物种绘制大西洋雨林退化和再生历史图。

Mapping Atlantic rainforest degradation and regeneration history with indicator species using convolutional network.

机构信息

Remote Sensing Division, National Institute for Space Research - INPE, São José dos Campos, SP, Brazil.

Geoprocessing Division, Foundation for Science, Technology and Space Applications - FUNCATE, São José dos Campos, SP, Brazil.

出版信息

PLoS One. 2020 Feb 28;15(2):e0229448. doi: 10.1371/journal.pone.0229448. eCollection 2020.

DOI:10.1371/journal.pone.0229448
PMID:32109946
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7048271/
Abstract

The Atlantic rainforest of Brazil is one of the global terrestrial hotspots of biodiversity. Despite having undergone large scale deforestation, forest cover has shown signs of increases in the last decades. Here, to understand the degradation and regeneration history of Atlantic rainforest remnants near São Paulo, we combine a unique dataset of very high resolution images from Worldview-2 and Worldview-3 (0.5 and 0.3m spatial resolution, respectively), georeferenced aerial photographs from 1962 and use a deep learning method called U-net to map (i) the forest cover and changes and (ii) two pioneer tree species, Cecropia hololeuca and Tibouchina pulchra. For Tibouchina pulchra, all the individuals were mapped in February, when the trees undergo mass-flowering with purple and pink blossoms. Additionally, elevation data at 30m spatial resolution from NASA Shuttle Radar Topography Mission (SRTM) and annual mean climate variables (Terraclimate datasets at ∼ 4km of spatial resolution) were used to analyse the forest and species distributions. We found that natural forests are currently more frequently found on south-facing slopes, likely because of geomorphology and past land use, and that Tibouchina is restricted to the wetter part of the region (southern part), which annually receives at least 1600 mm of precipitation. Tibouchina pulchra was found to clearly indicate forest regeneration as almost all individuals were found within or adjacent to forests regrown after 1962. By contrast, Cecropia hololeuca was found to indicate older disturbed forests, with all individuals almost exclusively found in forest fragments already present in 1962. At the regional scale, using the dominance maps of both species, we show that at least 4.3% of the current region's natural forests have regrown after 1962 (Tibouchina dominated, ∼ 4757 ha) and that ∼ 9% of the old natural forests have experienced significant disturbance (Cecropia dominated).

摘要

巴西的大西洋雨林是全球生物多样性的热点地区之一。尽管经历了大规模的森林砍伐,但在过去几十年中,森林覆盖率已经显示出增加的迹象。在这里,为了了解圣保罗附近大西洋雨林残余物的退化和再生历史,我们结合了来自 Worldview-2 和 Worldview-3 的高分辨率图像的独特数据集(分别为 0.5m 和 0.3m 的空间分辨率)、来自 1962 年的地理参考航空照片,并使用一种称为 U-net 的深度学习方法来绘制(i)森林覆盖和变化以及(ii)两种先锋树种 Cecropia hololeuca 和 Tibouchina pulchra。对于 Tibouchina pulchra,所有个体都在 2 月进行了绘制,此时树木会进行大规模开花,花朵呈现出紫色和粉红色。此外,还使用了来自 NASA 航天飞机雷达地形任务(SRTM)的 30m 空间分辨率的高程数据和年度平均气候变量(Terraclimate 数据集,空间分辨率约为 4km)来分析森林和物种分布。我们发现,自然森林目前更频繁地出现在朝南的山坡上,这可能是由于地貌和过去的土地利用方式,而 Tibouchina 则局限于该地区较湿润的部分(南部),该地区每年至少有 1600mm 的降雨量。发现 Tibouchina pulchra 可以清楚地表明森林再生,因为几乎所有个体都在 1962 年后重新生长的森林内或附近被发现。相比之下,发现 Cecropia hololeuca 表明存在较古老的受干扰森林,所有个体几乎都仅在 1962 年就存在的森林片段中被发现。在区域尺度上,使用这两个物种的优势度图,我们表明,至少有 4.3%的当前区域自然森林在 1962 年后已经重新生长(Tibouchina 占主导地位,约 4757ha),并且有 9%的古老自然森林经历了显著的干扰(Cecropia 占主导地位)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8123/7048271/bc9c39121250/pone.0229448.g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8123/7048271/32912daa449c/pone.0229448.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8123/7048271/57ef7e7cfcc3/pone.0229448.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8123/7048271/1c3e56397a7b/pone.0229448.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8123/7048271/ca9abea6e294/pone.0229448.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8123/7048271/720b802dbe50/pone.0229448.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8123/7048271/1ac564902c5b/pone.0229448.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8123/7048271/d38c83257baa/pone.0229448.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8123/7048271/3e59a9e7d478/pone.0229448.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8123/7048271/af5da041db58/pone.0229448.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8123/7048271/bc9c39121250/pone.0229448.g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8123/7048271/32912daa449c/pone.0229448.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8123/7048271/57ef7e7cfcc3/pone.0229448.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8123/7048271/1c3e56397a7b/pone.0229448.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8123/7048271/ca9abea6e294/pone.0229448.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8123/7048271/720b802dbe50/pone.0229448.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8123/7048271/1ac564902c5b/pone.0229448.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8123/7048271/d38c83257baa/pone.0229448.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8123/7048271/3e59a9e7d478/pone.0229448.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8123/7048271/af5da041db58/pone.0229448.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8123/7048271/bc9c39121250/pone.0229448.g010.jpg

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