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Modeling tropical montane forest biomass, productivity and canopy traits with multispectral remote sensing data

机译:采用多光谱遥感数据建模热带蒙太森林生物量,生产力和冠层特征

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Tropical montane forests, particularly Andean rainforest, are important ecosystems for regional carbon and water cycles as well as for biological diversity and speciation. Owing to their remoteness, however, ecological key-processes are less understood as in the tropical lowlands. Remote sensing allows modeling of variables related to spatial patterns of carbon stocks and fluxes (e.g., biomass) and ecosystem functioning (e.g., functional leaf traits). However, at a landscape scale most studies conducted so far are based on airborne remote sensing data which is often available only locally and for one time-point. In contrast, multispectral satellites at moderate spectral and spatial resolutions are able to provide spatially continuous and repeated observations. Here, we investigated the effectiveness of Landsat-8 imagery in modeling tropical montane forest biomass, its productivity and selected canopy traits. Topographical, spectral and textural metrics were derived as predictors. To train and validate the models, in-situ data was sampled in 54 permanent plots in forests of southern Ecuador distributed within three study sites at 1000 m, 2000 m and 3000 m a.s.l. We used partial least squares regressions to model and map all response variables. Along the whole elevation gradient biomass and productivity models explained 31%, 43%, 69% and 63% of variance in aboveground biomass, annual wood production, fine litter production and aboveground net primary production, respectively. Regression models of canopy traits measured as community weighted means explained 62%, 78%, 65% and 65% of variance in leaf toughness, specific leaf area, foliar N concentration, and foliar P concentration, respectively. Models at single study sites hardly explained variation in aboveground biomass and the annual wood production indicating that these measures are mainly determined by the change of forest types along with elevation. In contrast, the models of fine litter production and canopy trai
机译:热带山地森林,特别是安第斯雨林,是区域碳和水循环的重要生态系统,以及生物多样性和品种。然而,由于他们的偏远,生态关键过程不太理解为热带低地。遥感允许与碳储量(例如,生物量)和生态系统(例如功能叶状特征)建模的变量建模。然而,到目前为止,大多数研究所进行的大多数研究都是基于空中遥感数据,该数据通常仅在本地可用,并且是一个时间点。相反,在适度的光谱和空间分辨率下的多光谱卫星能够提供空间连续和重复的观察。在这里,我们调查了Landsat-8图像在造型的热带山料林生物量,其生产率和选定的冠层特征中的有效性。地形,光谱和纹理度量被推导为预测因素。为了培训和验证模型,在南欧南部森林的54个永久地块中取出了原位数据,分布在三个研究网站,在1000米,2000米和3000米A.L.我们使用部分最小二乘回归来模拟和映射所有响应变量。沿着整个海拔梯度生物质和生产力模型分别解释了地上生物质,年度木材生产,细垃圾生产和地上净初级生产的31%,43%,69%和63%。作为群落加权的冠层特征的回归模型分别解释了叶韧性,特异性叶面积,叶面N浓度和叶面P浓度的62%,78%,65%和65%。单一研究网站的模型几乎解释了地上生物量和年度木材生产的变化,表明这些措施主要由森林类型的变化以及高程决定。相比之下,精细垃圾生产和冠层的模型

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