Mapping the Spatial Distribution of Fern Thickets and Vine-Laden Forests in the Landscape of Bornean Logged-Over Tropical Secondary Rainforests
Mapping the Spatial Distribution of Fern Thickets and Vine-Laden Forests in the Landscape of Bornean Logged-Over Tropical Secondary Rainforests
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DOI：
10.3390/rs14143354
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发表时间：
2022-07
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通讯作者：
Ryuichi Takeshige;M. Onishi;Ryota Aoyagi;Yoshimi Sawada;N. Imai;R. Ong;K. Kitayama
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作者：
Ryuichi Takeshige;M. Onishi;Ryota Aoyagi;Yoshimi Sawada;N. Imai;R. Ong;K. Kitayama
森林退化最常被定义为与现有森林的参考生物量相比人为造成的生物量减少。然而，如此定义的“退化森林”在可恢复性方面可能存在很大差异。可恢复性的长期丧失（通常被称为恢复力丧失）对全球环境构成了真正的威胁。在婆罗洲被砍伐的森林中，人们观察到茂密的蕨类植物和藤本植物灌木丛会导致次生演替停滞，它们的面积可能表明生物量恢复缓慢的程度。因此，我们的目的是借助 Landsat-8 卫星图像和机器学习模型，区分蕨类灌木丛和藤蔓丛生的森林与没有茂密蕨类植物和藤蔓的被砍伐的森林，并绘制它们的分布图。在此过程中，我们测试了 Landsat 数据和 Sentinel-1 C 波段 SAR 数据的灰度共生矩阵 (GLCM) 纹理是否有助于这种分类。我们的研究地点是 Deramakot 和 Tangkulap 森林保护区——马来西亚婆罗洲沙巴的商业生产森林。首先，我们驾驶无人机并获取航空图像，将其用作监督分类的地面实况。随后，迭代测试了具有梯度提升决策树的机器学习模型，以得出植被分类的最佳模型。最后，将最佳模型外推到整个森林保护区，并用于绘制三类植被（蕨类灌木丛、藤蔓丛生的森林和没有蕨类植物和藤蔓的砍伐森林）和两类非植被（裸土和开放水域）。最佳模型的总体分类准确率为86.6%；然而，通过将蕨类植物和藤本植物类合并到同一类别中，准确率提高到了 91.5%。 GLCM 纹理变量对于将蕨类/藤本植被与未退化森林分开特别有效，但 SAR 数据显示效果有限。我们最终的植被地图显示，30.7% 的保护区被蕨类植物或藤本植物占据，这可能会导致继承停滞。考虑到我们的研究地点曾经被认证为管理良好的森林，预计其他婆罗洲生产林中具有高恢复力丧失风险的退化森林面积会更大。
Forest degradation has been most frequently defined as an anthropogenic reduction in biomass compared with reference biomass in extant forests. However, so-defined “degraded forests” may widely vary in terms of recoverability. A prolonged loss of recoverability, commonly described as a loss of resilience, poses a true threat to global environments. In Bornean logged-over forests, dense thickets of ferns and vines have been observed to cause arrested secondary succession, and their area may indicate the extent of slow biomass recovery. Therefore, we aimed to discriminate the fern thickets and vine-laden forests from those logged-over forests without dense ferns and vines, as well as mapping their distributions, with the aid of Landsat-8 satellite imagery and machine learning modeling. During the process, we tested whether the gray-level co-occurrence matrix (GLCM) textures of Landsat data and Sentinel-1 C-band SAR data were helpful for this classification. Our study sites were Deramakot and Tangkulap Forest Reserves—commercial production forests in Sabah, Malaysian Borneo. First, we flew drones and obtained aerial images that were used as ground truth for the supervised classification. Subsequently, a machine-learning model with a gradient-boosting decision tree was iteratively tested in order to derive the best model for the classification of the vegetation. Finally, the best model was extrapolated to the entire forest reserve and used to map three classes of vegetation (fern thickets, vine-laden forests, and logged-over forests without ferns and vines) and two non-vegetation classes (bare soil and open water). The overall classification accuracy of the best model was 86.6%; however, by combining the fern and vine classes into the same category, the accuracy was improved to 91.5%. The GLCM texture variables were especially effective at separating fern/vine vegetation from the non-degraded forest, but the SAR data showed a limited effect. Our final vegetation map showed that 30.7% of the reserves were occupied by ferns or vines, which may lead to arrested succession. Considering that our study site was once certified as a well-managed forest, the area of degraded forests with a high risk of loss of resilience is expected to be much broader in other Bornean production forests.