期刊
REMOTE SENSING IN ECOLOGY AND CONSERVATION
卷 7, 期 3, 页码 397-410出版社
WILEY
DOI: 10.1002/rse2.197
关键词
Hyperspectral imaging; liana infestation; LiDAR; neural network; pixel-based soft classification; segmentation
资金
- Natural Environment Research Council [NE/P004806/1, NE/I528477/1 (ARSF MA14/11), NE/L002604/1]
- FCT (FundacAo para a Ciencia e a Tecnologia) - Centro de InvestigacAo em GestAo de InformacAo (MagIC) [UIDB/04152/2020]
- University of Nottingham for an Anne McLaren Research Fellowship
- Fundação para a Ciência e a Tecnologia [UIDB/04152/2020] Funding Source: FCT
- NERC [NE/P004806/1] Funding Source: UKRI
This study effectively assessed liana infestation in aseasonal tropical forest using remote sensing, highlighting that a pixel-based classification was more effective in predicting liana infestation compared to an object-based approach. Differences in accuracy between object- and pixel-based classifications were not significant when liana infestation was grouped into three classes, indicating the potential usefulness of both methods for informing conservation and forest management efforts.
The ability to accurately assess liana (woody vine) infestation at the landscape level is essential to quantify their impact on carbon dynamics and help inform targeted forest management and conservation action. Remote sensing techniques provide potential solutions for assessing liana infestation at broader spatial scales. However, their use so far has been limited to seasonal forests, where there is a high spectral contrast between lianas and trees. Additionally, the ability to align the spatial units of remotely sensed data with canopy observations of liana infestation requires further attention. We combined airborne hyperspectral and LiDAR data with a neural network machine learning classification to assess the distribution of liana infestation at the landscape-level across an aseasonal primary forest in Sabah, Malaysia. We tested whether an object-based classification was more effective at predicting liana infestation when compared to a pixel-based classification. We found a stronger relationship between predicted and observed liana infestation when using a pixel-based approach (RMSD = 27.0% +/- 0.80) in comparison to an object-based approach (RMSD = 32.6% +/- 4.84). However, there was no significant difference in accuracy for object- versus pixel-based classifications when liana infestation was grouped into three classes; Low [0-30%], Medium [31-69%] and High [70-100%] (McNemar's chi(2) = 0.211, P = 0.65). We demonstrate, for the first time, that remote sensing approaches are effective in accurately assessing liana infestation at a landscape scale in an aseasonal tropical forest. Our results indicate potential limitations in object-based approaches which require refinement in order to accurately segment imagery across contiguous closed-canopy forests. We conclude that the decision on whether to use a pixel- or object-based approach may depend on the structure of the forest and the ultimate application of the resulting output. Both approaches will provide a valuable tool to inform effective conservation and forest management.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据