Using forestry inventories and satellite imagery to assess floristic variation in bamboo‐dominated forests in Peruvian Amazonia

Abstract

Questions: Does the floristic composition of trees differ between bamboo forests and adjacent non-bamboo forests? Can the degree of compositional differences be predicted from differences in canopy reflectance as measured by Landsat satellites? Are the results sensitive to different taxonomical data cleaning strategies, or to which tree-size class is considered? Are some tree taxa associated with either bamboo or non-bamboo forests? Location: Peruvian Amazonia. Methods: We used national forestry inventory data to characterise floristic composition of trees at 25 sites. Bamboo and non-bamboo forests were identified with a pixel-based time series analysis using the entire Landsat TM/ETM+ archive. To visualise floristic similarity among the plots, we used non-metric multidimensional scaling. Floristic differences between bamboo and non-bamboo forests were tested using an analysis of similarity (ANOSIM). Mantel tests were used to assess correlation between floristic turnover and differences in canopy reflectance. We tested the impact of applying three different taxonomic data cleaning strategies and of analysing different tree-size classes. Finally, we ran an indicator species analysis to identify taxa that were associated with either bamboo or non-bamboo forests. Results: In floristic ordinations, bamboo-dominated forests appeared floristically distinct from non-bamboo forests regardless of the taxonomic cleaning strategy and tree-size class. This floristic separation was also confirmed through analysis of floristic similarity (ANOSIM). Turnover in floristic composition was strongly correlated with differences in the reflectance values of Landsat bands, especially when using genus-level rather than species-level data. Different palm taxa were associated with bamboo (Socratea) and non-bamboo forests (Iriartea). Conclusions: Floristic differences of trees between bamboo-dominated and nonbamboo forests are consistent enough to be observable with different tree-size classes and taxonomic cleaning strategies. Although coarsening the taxonomic 2 of 15 |

Forest Biodiversity Monitoring Based on Remotely Sensed Spectral Diversity—A Review

“…Early multispectral time series approaches were published by Oindo 2002 [97] and Oindo & Skidmore 2002 [77] based on AVHRR data to investigate the species richness in Kenya. Since the year 2009, more and more studies integrated remote sensing time series data for the analysis of forest biodiversity using spectral diversity concepts [54,62,85,[98][99][100][101][102][103][104][105][106][107][108][109][110][111][112][113][114][115][116]. Studies that are solely based on LiDAR data have become specifically popular since the year 2020: on the one hand, all studies are based on mono-temporal remote sensing data, and on the other hand, the LiDAR data was derived from an airborne sensor [79,[117][118][119][120].…”

“…Studies that are solely based on LiDAR data have become specifically popular since the year 2020: on the one hand, all studies are based on mono-temporal remote sensing data, and on the other hand, the LiDAR data was derived from an airborne sensor [79,[117][118][119][120]. Another finding is that studies integrating multiple sensors have greatly increased in recent years: more than 55% of all reviewed studies based on remote sensing data from multiple sensors have been published since 2018 [54,[105][106][107]109,113,114,[121][122][123][124][125][126][127].…”