Mapping native and non-native vegetation in the Brazilian Cerrado using freely available satellite products.
Mapping native and non-native vegetation in the Brazilian Cerrado using freely available satellite products.
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使用可自由可用的卫星产品在巴西塞拉多州绘制本地和非本地植被。
DOI：
10.1038/s41598-022-05332-6
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发表时间：
2022-01-28
影响因子：
4.6
通讯作者：
Rowland L
中科院分区：
文献类型：
--
作者：
Lewis K;de V Barros F;Cure MB;Davies CA;Furtado MN;Hill TC;Hirota M;Martins DL;Mazzochini GG;Mitchard ETA;Munhoz CBR;Oliveira RS;Sampaio AB;Saraiva NA;Schmidt IB;Rowland L
巴西塞拉多的原生植被具有高度异质性和生物多样性，提供了重要的生态系统服务，包括碳和水平衡调节，然而，土地利用变化很大。保护和恢复当地植被至关重要，详细的土地覆盖图可有助于这一工作。在这里，在巴西戈亚斯州的一个大型案例研究区域（1.1 Mha），我们制作了原生植被（n = 8）和其他土地覆盖类型（n = 5）的地貌水平地图。使用了七种不同的分类方案，使用不同的输入卫星图像组合，并在Google Earth Engine中采用了随机森林分类器和两阶段方法。总体分类精度范围为88.6-92.6%的原生和非原生植被在形成水平（阶段-1），70.7-77.9%的原生植被在地貌水平（阶段-2），在7个不同的分类方案。不同的输入图像组合和使用的质量控制程序的分类精度的差异很小。然而，季节性哨兵-1号（C波段合成孔径雷达）和哨兵-2号（表面反射率）图像相结合，在20米的空间分辨率下进行了最准确的分类。使用Landsat-8图像时的分类准确度略低，但仍然合理。在选择植被参考数据时考虑植被燃烧的质量控制程序也可能提高某些原生植被类型的分类准确性。利用免费提供的卫星图像和可升级技术制作的详细土地覆盖图将是了解植被在景观尺度上的功能和实施恢复项目的重要工具。
Native vegetation across the Brazilian Cerrado is highly heterogeneous and biodiverse and provides important ecosystem services, including carbon and water balance regulation, however, land-use changes have been extensive. Conservation and restoration of native vegetation is essential and could be facilitated by detailed landcover maps. Here, across a large case study region in Goiás State, Brazil (1.1 Mha), we produced physiognomy level maps of native vegetation (n = 8) and other landcover types (n = 5). Seven different classification schemes using different combinations of input satellite imagery were used, with a Random Forest classifier and 2-stage approach implemented within Google Earth Engine. Overall classification accuracies ranged from 88.6–92.6% for native and non-native vegetation at the formation level (stage-1), and 70.7–77.9% for native vegetation at the physiognomy level (stage-2), across the seven different classifications schemes. The differences in classification accuracy resulting from varying the input imagery combination and quality control procedures used were small. However, a combination of seasonal Sentinel-1 (C-band synthetic aperture radar) and Sentinel-2 (surface reflectance) imagery resulted in the most accurate classification at a spatial resolution of 20 m. Classification accuracies when using Landsat-8 imagery were marginally lower, but still reasonable. Quality control procedures that account for vegetation burning when selecting vegetation reference data may also improve classification accuracy for some native vegetation types. Detailed landcover maps, produced using freely available satellite imagery and upscalable techniques, will be important tools for understanding vegetation functioning at the landscape scale and for implementing restoration projects.
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影响因子：
4.9
作者：
da Silva, Danilo Muniz;Batalha, Marco Antonio
通讯作者：
Batalha, Marco Antonio
影响因子：
1.1
作者：
Silva, Diogo Pereira da;Amaral, Aryanne G.;Munhoz, Cássia Beatriz R.
通讯作者：
Munhoz, Cássia Beatriz R.
影响因子：
5.5
作者：
Bueno, Marcelo Leandro;Dexter, Kyle G.;de Oliveira-Filho, Ary Teixeira
通讯作者：
de Oliveira-Filho, Ary Teixeira
DOI：
10.1007/bf02342599
发表时间：
1978-01-01
期刊：
VEGETATIO
影响因子：
--
作者：
EITEN, G
通讯作者：
EITEN, G
影响因子：
5
作者：
El Hajj, Mohammad;Baghdadi, Nicolas;Zribi, Mehrez
通讯作者：
Zribi, Mehrez