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Towards nationwide mapping of bamboo resources in the Philippines: testing the pixel-based and fractional cover approaches

机译:朝着菲律宾竹资源的全国范围内:测试基于像素和分数覆盖方法

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摘要

In tropical and subtropical countries, the awareness on the importance of bamboos to the environment and economy is increasing and so is the demand for spatial bamboo information. However, mapping bamboos especially those naturally grown has been challenging, as these grasses are often mixed with other land-use and land-cover (LULC). In this study, we used Sentinel 1 and Sentinel 2 remote sensing (RS) images, and their vegetation indices to accurately map the bamboos of Iloilo province in the Philippines using: (1) pixel-based method that mapped bamboos and other LULC at 10 m resolution, and (2) fractional cover method that mapped bamboo cover at 100 m resolution (% ha(-1)). A random forest model was trained for each method and then validated per hectare basis using a 50:50 training-validation ratio of a stratified random sample. The fractional cover method showed 0.34 higher Nash-Sutcliffe Efficiency (NSE) and 5.10% lower Root Mean Square Error (RMSE) than the pixel-based method. Further validation within upland and lowland sites also favoured the fractional cover method, but the results of the two methods were closer in the upland site (bamboo plantation). Errors at 10 m resolution especially in the lowlands were mostly commission errors, likely because of the spectral similarity and proximity between bamboos and 14 vegetations. Averaging the RS inputs into 100 m resulted in at most 12% separation of reflectance values among bamboos, forests, and other vegetations. Using the bamboo cover map, a total of 14,795 (+/- 1,283) ha bamboos and 7.45 (+/- 4.20) million harvestable culms (poles) were estimated for the whole province, where 54% come from the lowland. We suggest using the fractional cover method for nationwide baselining of bamboo resources.
机译:在热带和亚热带的国家,对竹子对环境和经济的重要性的认识正在增加,空间竹信息的需求也在增加。然而,由于这些草通常与其他土地利用和陆地(LULC)混合,因此拍摄竹子尤其是自然生长的挑战。在本研究中,我们使用Sentinel 1和Sentinel 2遥感(RS)图像,以及它们的植被指数,以便使用:(1)基于像素的方法在10时映射竹子和其他LULC的基于像素的方法M分辨率,(2)分辨率绘制竹盖的分辨率和(2)分数覆盖方法(%HA(-1))。每种方法培训一次随机森林模型,然后使用分层随机样品的50:50训练验证率,每公顷验证。分数覆盖方法显示出比基于像素的方法的效率(NSE)效率(NSE)效率(NSE)和5.10%较低的均方根误差(RMSE)。高地和低地网站内的进一步验证也赞成分数覆盖方法,但两种方法的结果在高地部位(竹种植园)中更接近。在10米的分辨率下误差特别是在低地主要是佣金误差,可能是因为竹子和&gt之间的光谱相似性和邻近。 14植被。将RS输入平均到100米,导致竹子,森林和其他植被之间的反射率值的最多12%。使用竹盖地图,全省估计了总共14,795(+/- 1,283)HA Bamboos和7.45(+/- 4.20)百万种可收索的秆(Poles),其中54%来自低地。我们建议使用竹资源全国基地的分数覆盖方法。

著录项

  • 来源
    《International journal of remote sensing》 |2021年第10期|3380-3404|共25页
  • 作者单位

    Univ Philippines Coll Forestry & Nat Resources Inst Renewable Nat Resources Los Banos Philippines;

    Wageningen Univ & Res Environm Sci Grp Lab Geoinformat Sci & Remote Sensing Droevendaalsesteeg 3 NL-6708 PB Wageningen Netherlands|Wageningen Univ & Res Environm Sci Grp Environm Syst Anal Wageningen Netherlands;

    Univ Philippines Grad Sch Los Banos Philippines;

    Univ Philippines Coll Forestry & Nat Resources Dept Forest Biol Sci Los Banos Philippines;

    Univ Philippines Coll Forestry & Nat Resources Dept Forest Biol Sci Los Banos Philippines;

    Univ Philippines Coll Forestry & Nat Resources Dept Forest Prod & Paper Sci Los Banos Philippines;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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