首页> 外文期刊>Journal of Vegetation Science >Hyper-temporal remote sensing helps in relating epiphyllous liverworts and evergreen forests.
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Hyper-temporal remote sensing helps in relating epiphyllous liverworts and evergreen forests.

机译:超时态遥感有助于将附生的艾蒿和常绿森林联系起来。

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Question: Is there, at the macro-habitat scale, a relationship between the fraction of evergreen forests and the presence probability of epiphyllous liverworts? Can these two parameters be estimated and mapped using an NDVI-based indicator that is derived from time-series of SPOT-VGT imagery? Location: Southern China. Methods: Applying the ISODATA algorithm, we classified SPOT-VGT NDVI time-series imagery and produced an NDVI map at 1-km2 resolution containing 128 NDVI classes. That map and the National Land Cover Map of China (2000) were used to prepare a scheme for field sampling. For 537 1-km2 areas, located in 19 blocks of 100 km x 100 km, field data were collected. They represented 51 preselected NDVI classes that were assumed to contain evergreen forests. Data on the fractions of forest and evergreen forest, the fraction of evergreen forest in forest present and presence probability of epiphyllous liverworts were regressed against different NDVI class-specific indicators by means of weighted least-squares regression (WLSR). Results: The SPOT-VGT NDVI for March was found to best explain the variation between 1-km2 areas in the presence probability of epiphyllous liverworts (R2=0.64; linear relationship; RMSE=0.38) as the area fraction (%) of evergreen forest (R2=0.90; exponential relationship; RMSE=6.1%). Epiphyllous liverworts were only found within 1-km2 areas when the SPOT-VGT NDVI value for March was more than 0.50 (model estimate: 0.43+or-0.20) and the fraction of evergreen forest in 1 km2 was above 14% (model estimate: 5+or-35%). The estimation errors (with 95% confidence interval) of these two relationships were calculated using a bootstrap resampling method with 1000 replications; they were, respectively, 0.49-0.76 and 0.85-0.94 for R2, and 0.11-0.23 and 5.2-11.9% for RMSE. Other positive relationships were found between the presence probability of epiphyllous liverworts and the fraction of evergreen forest (R2=0.64, linear relationship; RMSE=0.38) and between the fraction of evergreen forest and forest within 1-km2 areas (R2=0.80, linear relationship; RMSE=29%). Conclusion: We show that for southern China, the fraction of evergreen forest and the presence probability of epiphyllous liverworts can directly be inferred by making use of SPOT-VGT NDVI imagery. The findings are fully consistent with earlier reported hotspots for epiphyllous liverworts. At the macro-habitat scale, the presence probability of epiphyllous liverworts proved quantitatively related to the fraction of evergreen forest. This suggests causal relationships with patch size and/or rainfall patterns. We believe that the derived maps may serve as a foundation for a range of further studies.Digital Object Identifier http://dx.doi.org/10.1111/j.1654-1103.2012.01453.x
机译:问题:在宏观生境尺度上,常绿森林的比例与epi生艾蒿存在概率之间是否存在关系?是否可以使用源自SPOT-VGT影像时间序列的基于NDVI的指标来估算和映射这两个参数?地点:中国南方。方法:采用ISODATA算法,对SPOT-VGT NDVI时间序列图像进行分类,制作出分辨率为1 km 2 的NDVI地图,其中包含128个NDVI类别。该地图和《中国国家土地覆盖图》(2000年)用于准备野外采样方案。对于位于100 km x 100 km的19个街区中的537个1-km 2 区域,收集了现场数据。他们代表了51个预选的NDVI类,这些类被假定为包含常绿森林。通过加权最小二乘回归(WLSR),针对不同的NDVI类别指标,对森林和常绿森林的比例,森林中常绿森林的比例以及and生艾蒿存在概率的数据进行了回归。结果:发现3月份的SPOT-VGT NDVI可以最好地解释1 km 2 区域在epi生艾草的存在概率下的变化(R 2 = 0.64;线性关系; RMSE = 0.38)作为常绿森林的面积分数(%)(R 2 = 0.90;指数关系; RMSE = 6.1%)。当三月份的SPOT-VGT NDVI值大于0.50(模型估计:0.43+或-0.20)且常绿森林的分数在1 km之内时,仅在1 km 2 地区发现附生的艾蒿。 2 高于14%(模型估计:5+或-35%)。这两个关系的估计误差(置信区间为95%)是使用具有1000个重复的自举重采样方法计算得出的; R 2 分别为0.49-0.76和0.85-0.94,RMSE分别为0.11-0.23和5.2-11.9%。在花positive的存在概率与常绿森林分数之间存在其他正相关关系(R 2 = 0.64,线性关系; RMSE = 0.38)以及常绿森林分数与1-内的森林之间km 2 区域(R 2 = 0.80,线性关系; RMSE = 29%)。结论:我们表明,对于中国南部地区,常绿森林的比例和epi生艾蒿的存在概率可以通过SPOT-VGT NDVI图像直接推断出来。该发现与早期报道的epi生艾蒿热点完全一致。在宏观生境尺度上,证明了附生艾蒿的存在概率与常绿森林的分数定量相关。这表明与斑块大小和/或降雨模式的因果关系。我们相信派生的地图可以作为进一步研究的基础。数字对象标识符http://dx.doi.org/10.1111/j.1654-1103.2012.01453.x

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