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首页> 外文期刊>Remote Sensing >Predictions of Tropical Forest Biomass and Biomass Growth Based on Stand Height or Canopy Area Are Improved by Landsat-Scale Phenology across Puerto Rico and the U.S. Virgin Islands
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Predictions of Tropical Forest Biomass and Biomass Growth Based on Stand Height or Canopy Area Are Improved by Landsat-Scale Phenology across Puerto Rico and the U.S. Virgin Islands

机译：波多黎各和美属维尔京群岛的陆地尺度尺度物候学改进了基于林分高度或冠层面积的热带森林生物量和生物量增长的预测。

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Remotely-sensed estimates of forest biomass are usually based on various measurements of canopy height, area, volume or texture, as derived from LiDAR, radar or fine spatial resolution imagery. These measurements are then calibrated to estimates of stand biomass that are primarily based on tree stem diameters. Although humid tropical forest seasonality can have low amplitudes compared with temperate regions, seasonal variations in growth-related factors like temperature, humidity, rainfall, wind speed and day length affect both tropical forest deciduousness and tree height-diameter relationships. Consequently, seasonal patterns in spectral measures of canopy greenness derived from satellite imagery should be related to tree height-diameter relationships and hence to estimates of forest biomass or biomass growth that are based on stand height or canopy area. In this study, we tested whether satellite image-based measures of tropical forest phenology, as estimated by indices of seasonal patterns in canopy greenness constructed from Landsat satellite images, can explain the variability in forest deciduousness, forest biomass and net biomass growth after already accounting for stand height or canopy area. Models of forest biomass that added phenology variables to structural variables similar to those that can be estimated by LiDAR or very high-spatial resolution imagery, like canopy height and crown area, explained up to 12% more variation in biomass. Adding phenology to structural variables explained up to 25% more variation in Net Biomass Growth (NBG). In all of the models, phenology contributed more as interaction terms than as single-effect terms. In addition, models run on only fully-forested plots performed better than models that included partially-forested plots. For forest NBG, the models produced better results when only those plots with a positive growth, from Inventory Cycle 1 to Inventory Cycle 2, were analyzed, as compared to models that included all plots
机译：对森林生物量的遥感估计通常基于对冠层高度，面积，体积或纹理的各种测量，这些测量是从LiDAR，雷达或精细的空间分辨率图像得出的。然后将这些测量值校准为主要基于树茎直径的林分生物量估计值。尽管与温带地区相比，潮湿的热带森林季节变化幅度可能较低，但与生长相关的因素（如温度，湿度，降雨量，风速和日长）的季节性变化会影响热带森林的落叶性和树木的高度-直径关系。因此，从卫星图像得出的冠层绿色度的光谱测量中的季节性模式应与树高-直径关系相关，并因此应与基于林分高度或冠层面积的森林生物量或生物量增长的估计有关。在这项研究中，我们测试了由Landsat卫星图像构建的冠层绿色度季节模式指数所估计的基于卫星图像的热带森林物候指标测度是否可以解释已经核算的森林落叶性，森林生物量和净生物量增长的变化性用于站立高度或顶篷面积。森林生物量模型将物候变量添加到结构变量中，类似于可以通过LiDAR或非常高空间分辨率的图像（如树冠高度和树冠面积）估计的变量，可以解释多达12％的生物量变化。在结构变量中增加物候特性可以解释净生物量增长（NBG）最多增加25％的变化。在所有模型中，物候作为相互作用项的贡献要大于单效应项。此外，仅在完全森林地块上运行的模型比包含部分森林地块的模型表现更好。对于森林NBG，与包括所有样地的模型相比，当仅分析那些从清单周期1到清单周期2呈正增长的样地时，模型产生了更好的结果。

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