The variation of apparent crown size and canopy heterogeneity across lowland Amazonian forests

Abstract

International audienceAim: The size structure of a forest canopy is an important descriptor of the forest environment that may yield information on forest biomass and ecology. However, its variability at regional scales is poorly described or understood because of the still prohibitive cost of very high-resolution imagery as well as the lack of an appropriate methodology. We here employ a novel approach to describe and map the canopy structure of tropical forests. Location Amazonia. Methods: We apply Fourier transform textural ordination (FOTO) techniques to subsamples of very high-resolution satellite imagery freely available through virtual globe software (e.g. Google Earth®) to determine two key structural variables: apparent mean crown size and heterogeneity in crown size. A similar approach is used with artificial forest canopy images generated by the light interaction model (discrete anisotropic radiative transfer, DART) using three-dimensional stand models. The effects of sun and viewing angles are explored on both model and real data. Results: It is shown that in the case of canopies dominated by a modal size class our approach can predict mean canopy size to an accuracy of 5%. In Amazonia, we could evidence a clear macrostructure, despite considerable local variability. Apparent crown size indeed consistently increases from about 14 m in wet north-west Amazonia to more than 17 m for areas of intermediate dry season length (1–3 months) in south and east Amazonia, before decreasing again towards the ecotone with the Cerrado savanna biome. This general trend reflects the known variation of other forest physiognomic properties (height) reported for South America and Africa. Some regions show significantly greater canopy heterogeneity, a feature that may be related to substratum, perturbation rate and/or forest turnover rate. Main conclusions: Our results demonstrate the feasibility and interest of large-scale assessment of rain forest canopy structur

Aboveground-Biomass Estimation of a Complex Tropical Forest in India Using Lidar

“…[39] demonstrated that crown allometries are less affected than stem allometries to site factors, making the former more suitable for predicting forest parameters. In terms of texture, the selection of the third PCA axis (FC3 or FD3) is somehow contradictory with previous studies where it was noted that most of the textural information is synthetized along the two first PCA axes [54]. However, additional research is needed to fully understand the link between FOTO metrics and forest parameters in a range of forest conditions.…”

Biome-Scale Forest Properties in Amazonia Based on Field and Satellite Observations

“…A macrostructure of crown size has been detected by [55] in Amazonia terra firme forests. Despite the high local variability, apparent crown size tends to be larger than 16 m in a broad east-west band, from 5-10° of the equator, and larger than 15 m in the contiguous region in north-eastern Amazonia.…”

Wavelet Based Analysis of TanDEM-X and LiDAR DEMs across a Tropical Vegetation Heterogeneity Gradient Driven by Fire Disturbance in Indonesia

“…Developing methods for the extraction of texture is a key priority to retrieve information on canopy conditions from forest structure [32]. However, texture derived from 3D InSAR datasets has not yet been explored to obtain information related to canopy heterogeneity and in particular for the analysis of differences in structure between landscapes, which are characterized by a gradient of heterogeneity derived from anthropogenic disturbance.…”

“…Also forest disturbance has been addressed using these techniques (e.g., selective logging: [52,53]). Canopy grain analysis from FOTO applied to LiDAR derived Canopy Height Model (CHM) and Digital Surface Model (DSM) was also used to generate metrics related to structure and to ultimately improve above-ground biomass prediction [32,54]. The extraction of single crowns from LiDAR has also been explored [55].…”

“…They were estimated to make up 60% of the total area classified as forest in tropical regions covering 850 million ha in the year 2000 [4], high resolution optical imagery has been employed successfully for discrimination of forest types due to the ability to exploit the variation in illumination between crowns, this resulting in tonal variation between the sunlit and shadow canopy components using Fourier Transform Textural Ordination (FOTO) [32,47,49]. Lacunarity has also been explored for the analysis of spatial patterns derived from canopy arrangements in simulated LiDAR datasets [26] and in high resolution multi-spectral optical data [24].…”