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首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >Mapping spatial distribution and biomass of coastal wetland vegetation in Indonesian Papua by combining active and passive remotely sensed data
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Mapping spatial distribution and biomass of coastal wetland vegetation in Indonesian Papua by combining active and passive remotely sensed data

机译：结合主动和被动遥感数据绘制印度尼西亚巴布亚沿海湿地植被的空间分布和生物量

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There is ongoing interest to develop remote sensing methods for mapping and monitoring the spatial distribution and biomass of mangroves. In this study, we develop a suite of methods to evaluate the combination of Landsat-8, ALOS PALSAR, and SRTM data for mapping spatial distribution of mangrove composition, canopy height, and aboveground biomass in the wide intertidal zones and coastal plains of Mimika district, Papua, Indonesia. Image segmentation followed by visual interpretation of composite PALSAR images was used to delineate mangrove areas, whereas a flexible statistical rule based classification of spectral signatures from Landsat-8 images was used to classify mangrove associations. The overall accuracy of land cover classification was 94.38% with a kappa coefficient of 0.94 when validated with field inventory data and Google Earth images. Mangrove height and aboveground biomass were mapped using the SRTM DEM, which were calibrated with field-measured data via quantile regression models. There was a strong correlation between the SRTM DEM and the 0.98 quantile of field canopy heights (H-.98), which was used to represent the tallest trees in each of 196 10 m radius subplots (r = 0.84 and R-2 = 0.804). Model performance was evaluated through 10,000 bootstrapped simulations, producing a mean absolute error (MAE) of 3.0 m for canopy height estimation over 30 m pixels of SRTM data. Quantile regression revealed a relatively strong non-linear relationship between the SRTM derived canopy height model and aboveground biomass measured in 0.5 ha mangrove inventory plots (n = 33, R-2 = 0.46). The model results produced estimates of mean standing biomass of 237.52 +/- 982 Mg/ha in short canopy (Avicennia/Sonneratia) stands to 353.52 +/- 98.43 Mg/ha in mature tall canopy (Rhizophora) dominated forest. The model estimates of mangrove biomass were within 90% confidence intervals of area-weighted biomass derived from field measurements. When validated at the landscape scale, the difference between modeled and measured aboveground mangrove biomass was 3.48% with MAE of 105.75 Mg/ha. These results indicate that the approaches developed here are reliable for mapping and monitoring mangrove composition, height, and biomass over large areas of Indonesia. (C) 2016 Elsevier Inc. All rights reserved.
机译：人们一直在研究开发遥感方法，以绘制和监测红树林的空间分布和生物量。在这项研究中，我们开发了一套方法来评估Landsat-8，ALOS PALSAR和SRTM数据的组合，以绘制Mimika地区宽潮间带和沿海平原中红树林组成，冠层高度和地上生物量的空间分布，印度尼西亚巴布亚。图像分割后，通过视觉解释合成的PALSAR图像来描绘红树林区域，而使用基于Landsat-8图像的基于统计规则的灵活统计规则分类来对红树林关联进行分类。经实地盘点数据和Google Earth图像验证后，土地覆被分类的总体准确性为94.38％，kappa系数为0.94。使用SRTM DEM绘制红树林高度和地上生物量，并通过分位数回归模型与实地测量数据进行校准。 SRTM DEM与0.98视场冠层高度（H-.98）之间有很强的相关性，该高度用于表示196个10 m半径子图中的每个最高树（r = 0.84和R-2 = 0.804） ）。通过10,000次引导仿真评估了模型性能，对于SRTM数据的30 m像素上的树冠高度估计，产生了3.0 m的平均绝对误差（MAE）。分位数回归显示，SRTM得出的冠层高度模型与在0.5公顷的红树林库存区（n = 33，R-2 = 0.46）中测得的地上生物量之间存在较强的非线性关系。该模型结果估算出，短冠层（Avicennia / Sonneratia）的平均站立生物量为237.52 +/- 982 Mg / ha，成熟的高冠层（Rhizophora）为主的森林的平均站立生物量为353.52 +/- 98.43 Mg / ha。红树林生物量的模型估算值在现场测量得出的面积加权生物量的90％置信区间内。在景观规模上进行验证时，模拟和测量的地上红树林生物量之间的差异为3.48％，MAE为105.75 Mg / ha。这些结果表明，这里开发的方法对于印度尼西亚大片地区的红树林组成，高度和生物量的制图和监测是可靠的。 （C）2016 Elsevier Inc.保留所有权利。

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