Discrimination of tropical forest types, dominant species, and mapping of functional guilds by hyperspectral and simulated multispectral Sentinel-2 data

Abstract

a b s t r a c tTo answer new scientific and ecological questions and monitor multiple forest changes, a fine scale characterization of these ecosystems is needed, and could imply the mapping of specific species, of detailed forest types, and of functional composition. This characterization can be now provided by the novel Earth Observation tools. This study aims to contribute to understanding the innovation in forest and ecological research that can be brought in by advanced remote sensing instruments, and proposes the guild mapping approach as a tool to efficiently monitor the varied tropical forest resources. We evaluated, in tropical Ghanaian forests, the ability of airborne hyperspectral and simulated multispectral Sentinel-2 data, and derived vegetation indices and textures, to: distinguish between two different forest types; to discriminate among selected dominant species; and to separate trees species grouped according to their functional guilds: Pioneer, Non Pioneer Light Demanding, and Shade Bearer. We then produced guild classification maps for each area using hyperspectral data. Our results showed that with both hyperspectral and simulated Sentinel-2 data these discrimination tasks can be successfully accomplished. Results also stressed the importance of texture features, especially if using the lower spectral and spatial Sentinel-2 resolution data, and highlighted the important role of the new Sentinel-2 data for ecological monitoring. Classification results showed a statistically significant improvement in overall accuracy using Support Vector Machine, over Maximum Likelihood approach. We proposed the functional guilds mapping as an innovative approach to: (i) monitor compositional changes, especially with respect to the effects of global climate change on forests, and particularly in the tropical biome where the occurrence of hundreds of species prevents mapping activities at species level; (ii) support large-scale forest inventories. The imminent Sentinel-2 data could serve to open the road for the development of new concepts and methods in forestry and ecological research..

Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics

“…In addition to recording tree species taxonomy and distribution, the monitoring of forest populations and forest community structures [471] or forest types is also possible using RS, although it is also subject to the same constraints as for the discrimination of tree species. Ioki et al, [472] assessed the similarity between tree community compositions in a tropical rainforest using airborne LiDAR RS data, whereas Laurin et al, [436] recorded tropical forest types, dominant species, and functional guilds in FES by using hyperspectral and simulated multispectral Sentinel-2 data.…”

“…This ranges from analysing the composition, configuration and distribution of phylogenetic structures [89] to molecular biochemical structures and patterns in plants [21,489,490] to 2/3 D individual geometry and structure [491][492][493], populations and community structures [349,435,436] and the recording of patterns and fragmentation in FES [494]. Further examples of monitoring stress and changes to the functional and structural diversity can also be recorded very well using RS.…”

“…• Turnover in species composition like spatial heterogeneity and dissimilarity [258,349,434] or spatial structure and distribution of species composition, tree community turnover [435][436][437].…”

Precision forestry: concepts, tools and perspectives in Italy

“…2 -Composizione in falso colore (rosso 829 nm, verde 604 nm, blu 465 nm) di immagini multispettrali Sentinel-2 (risoluzione geometrica: 1 metro, risoluzione spettrale: 244 canali) in due Parchi Nazionali del Ghana con sensore aviotrasportato. I poligoni in giallo rappresentano le chiome di soggetti arborei delle singole specie di interesse, individuate utilizzando software GIS (Laurin et al 2016). (Scrinzi & Clementel 2015b) consolidata, i più recenti sviluppi tecnologici hanno aperto nuove frontiere mediante l'utilizzo dell'informazione tridimensionale fornita dai sistemi di telerilevamento attivo (radiazione elettromagnetica emessa dal sensore) che utilizzano tecniche LiDAR (radiazione emessa nel campo visibile -infrarosso) da piattaforma aerea (ALS - Fig.…”

Methodology for Determining Optimal Exposure Parameters of a Hyperspectral Scanning Sensor

“…Those data type, in comparison to the multispectral data significantly had facilitate the process of identification and analysis of investigated objects (e.g. Zagajewski, 2010;J Quin, 2012;Mariotto et al, 2012;Sawicki, 2012;Jianwei et al, 2013;Kedzierski et al, 2014;Marshall and Thenkabail, 2015;Jiangbo Li et al, 2016;Vaglio et al, 2016). The combination of the information capacity of hundreds of spectral channels with a relatively rapid data acquisition, certainly has a great impact on dynamic development of remote sensing techniques.…”