Using airborne laser scanning to characterize land-use systems in a tropical landscape based on vegetation structural metrics

Nicolò Camarretta*, Martin Ehbrecht, Dominik Seidel, Arne Wenzel, Mohd Zuhdi, Miryam Sarah Merk, M. Schlund, Stefan Erasmi, Alexander Knohl

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

15 Citations (Scopus)
61 Downloads (Pure)

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.

Original languageEnglish
Article number4794
Number of pages23
JournalRemote sensing
Volume13
Issue number23
DOIs
Publication statusPublished - 26 Nov 2021

Keywords

  • Airborne LiDAR
  • ANOVA
  • Land management
  • Land use characterization
  • PCA
  • Random forest
  • Vegetation structure
  • ITC-ISI-JOURNAL-ARTICLE
  • ITC-GOLD

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