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利用陆地卫星图像绘制菲律宾的红树林分布图。

Mapping the Philippines' mangrove forests using Landsat imagery.

机构信息

US Geological Survey, Earth Resources Observation and Science Center, Sioux Falls, SD 57198, USA.

出版信息

Sensors (Basel). 2011;11(3):2972-81. doi: 10.3390/s110302972. Epub 2011 Mar 7.

DOI:10.3390/s110302972
PMID:22163779
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3231605/
Abstract

Current, accurate, and reliable information on the areal extent and spatial distribution of mangrove forests in the Philippines is limited. Previous estimates of mangrove extent do not illustrate the spatial distribution for the entire country. This study, part of a global assessment of mangrove dynamics, mapped the spatial distribution and areal extent of the Philippines' mangroves circa 2000. We used publicly available Landsat data acquired primarily from the Global Land Survey to map the total extent and spatial distribution. ISODATA clustering, an unsupervised classification technique, was applied to 61 Landsat images. Statistical analysis indicates the total area of mangrove forest cover was approximately 256,185 hectares circa 2000 with overall classification accuracy of 96.6% and a kappa coefficient of 0.926. These results differ substantially from most recent estimates of mangrove area in the Philippines. The results of this study may assist the decision making processes for rehabilitation and conservation efforts that are currently needed to protect and restore the Philippines' degraded mangrove forests.

摘要

目前,有关菲律宾红树林的面积和空间分布的准确、可靠信息有限。以前对红树林范围的估计并未说明整个国家的空间分布情况。这项研究是对红树林动态进行全球评估的一部分,绘制了菲律宾大约 2000 年的红树林空间分布和面积。我们使用了从全球土地调查中获取的公开可用的 Landsat 数据来绘制总面积和空间分布。ISODATA 聚类是一种无监督分类技术,应用于 61 张 Landsat 图像。统计分析表明,大约在 2000 年,菲律宾的红树林总面积约为 256185 公顷,总体分类精度为 96.6%,kappa 系数为 0.926。这些结果与菲律宾最近对红树林面积的估计有很大不同。这项研究的结果可能有助于决策过程,以进行修复和保护工作,目前需要保护和恢复菲律宾退化的红树林。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a9/3231605/f0a7accfb3d4/sensors-11-02972f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a9/3231605/d1e8735cb7b7/sensors-11-02972f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a9/3231605/492484e41d4c/sensors-11-02972f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a9/3231605/20bef1f82489/sensors-11-02972f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a9/3231605/0c8aa78c7655/sensors-11-02972f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a9/3231605/7fd30a3ef1f0/sensors-11-02972f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a9/3231605/3c28526bbecc/sensors-11-02972f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a9/3231605/a52de490e07b/sensors-11-02972f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a9/3231605/f0a7accfb3d4/sensors-11-02972f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a9/3231605/d1e8735cb7b7/sensors-11-02972f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a9/3231605/492484e41d4c/sensors-11-02972f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a9/3231605/20bef1f82489/sensors-11-02972f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a9/3231605/0c8aa78c7655/sensors-11-02972f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a9/3231605/7fd30a3ef1f0/sensors-11-02972f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a9/3231605/3c28526bbecc/sensors-11-02972f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a9/3231605/a52de490e07b/sensors-11-02972f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a9/3231605/f0a7accfb3d4/sensors-11-02972f8.jpg

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