Abstract
Many Indonesian forests have been cleared and replaced by fast-growing cash crops (e.g., oil palm and rubber plantations), altering the vegetation structure of entire regions. Complex vegetation structure provides habitat niches to a large number of native species. Airborne laser scanning (ALS) can provide detailed three-dimensional information on vegetation structure. Here, we investigate the potential of ALS metrics to highlight differences across a gradient of land-use management intensities in Sumatra, Indonesia. We focused on tropical rainforests, jungle rubber, rubber plantations, oil palm plantations and transitional lands. Twenty-two ALS metrics were extracted from 183 plots. Analysis included a principal component analysis (PCA), analysis of variance (ANOVAs) and random forest (RF) characterization of the land use/land cover (LULC). Results from the PCA indicated that a greater number of canopy gaps are associated with oil palm plantations, while a taller stand height and higher vegetation structural metrics were linked with rainforest and jungle rubber. A clear separation in metrics performance between forest (including rainforest and jungle rubber) and oil palm was evident from the metrics pairwise comparison, with rubber plantations and transitional land behaving similar to forests (rainforest and jungle rubber) and oil palm plantations, according to different metrics. Lastly, two RF models were carried out: one using all five land uses (5LU), and one using four, merging jungle rubber with rainforest (4LU). The 5LU model resulted in a lower overall accuracy (51.1%) due to mismatches between jungle rubber and forest, while the 4LU model resulted in a higher accuracy (72.2%). Our results show the potential of ALS metrics to characterize different LULCs, which can be used to track changes in land use and their effect on ecosystem functioning, biodiversity and climate.
Highlights
Land-use change and land degradation represent key contributors to the increase in greenhouse gas emissions, both globally [1] and in the tropics [2,3], while having an important impact on the habitat quality for many native species [4]
The overall variation across the 22 LiDAR-derived metrics is summarized by the principal component analysis (Figure 4)
The first two principal components (PC1 and PC2 shown in Figure 4) accounted for 64% and 14.7% of the total variance among samples, while PC3 and PC4 only accounted for 8.1% and 3.3%, respectively
Summary
Land-use change and land degradation represent key contributors to the increase in greenhouse gas emissions, both globally [1] and in the tropics [2,3], while having an important impact on the habitat quality for many native species [4]. Vegetation structural complexity is considered a reliable proxy for ecosystem biodiversity and habitat quality, as it can provide insights into several ecosystem functions as well as their overall health [8,9,10,11]. Transformed land-use systems—depending on their management intensity—might hold important vegetation structural complexity and ecosystem functions. It is of paramount importance to acquire detailed knowledge on the vegetation structural complexity of different land uses in a landscape for the effective management of highly modified tropical landscapes [29]. Airborne or spaceborne remote sensing technologies can offer viable alternatives to traditional fieldwork, being more repeatable and less affected by human measurement errors [30]
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