TY - JOUR
T1 - UAV RGB, thermal infrared and multispectral imagery used to investigate the control of terrain on the spatial distribution of dryland biocrust
AU - Blanco-Sacristán, Javier
AU - Panigada, Cinzia
AU - Gentili, Rodolfo
AU - Tagliabue, Giulia
AU - Garzonio, Roberto
AU - Martín, M. Pilar
AU - Ladrón de Guevara, Mónica
AU - Colombo, Roberto
AU - Dowling, Thomas P.F.
AU - Rossini, Micol
N1 - Funding Information:
This research has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 721995. It also received funding by MIUR – Dipartimenti di Eccellenza 2018–2022. The work of M.L.d.G was supported by the European Research Council grant agreement no. 647038 (BIODESERT). The authors acknoledge Fabio Moia and Davide Abu El Khair (University of Milano-Bicocca) for soil laboratory measurements.
Funding Information:
This research has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska‐Curie grant agreement No 721995. It also received funding by MIUR – Dipartimenti di Eccellenza 2018–2022. The work of M.L.d.G was supported by the European Research Council grant agreement no. 647038 (BIODESERT). The authors acknoledge Fabio Moia and Davide Abu El Khair (University of Milano‐Bicocca) for soil laboratory measurements.
Publisher Copyright:
© 2021 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.
PY - 2021/9/30
Y1 - 2021/9/30
N2 - Biocrusts (topsoil communities formed by mosses, lichens, bacteria, fungi, algae, and cyanobacteria) are a key biotic component of dryland ecosystems. Whilst climate patterns control the distribution of biocrusts in drylands worldwide, terrain and soil attributes can influence biocrust distribution at landscape scale. Multi-source unmanned aerial vehicle (UAV) imagery was used to map and study biocrust ecology in a typical dryland ecosystem in central Spain. Red, green and blue (RGB) imagery was processed using structure-from-motion techniques to map terrain attributes related to microclimate and terrain stability. Multispectral imagery was used to produce accurate maps (accuracy > 80%) of dryland ecosystem components (vegetation, bare soil and biocrust composition). Finally, thermal infrared (TIR) and multispectral imagery was used to calculate the apparent thermal inertia (ATI) of soil and to evaluate how ATI was related to soil moisture (r2 = 0.83). The relationship between soil properties and UAV-derived variables was first evaluated at the field plot level. Then, the maps obtained were used to explore the relationship between biocrusts and terrain attributes at ecosystem level through a redundancy analysis. The most significant variables that explain biocrust distribution are: ATI (34.4% of variance, F = 130.75; p < 0.001), Elevation (25.8%, F = 97.6; p < 0.001), and potential solar incoming radiation (PSIR) (52.9%, F = 200.1; p < 0.001). Differences were found between areas dominated by lichens and mosses. Lichen-dominated biocrusts were associated with areas with high slopes and low values of ATI, with soil characterized by a higher amount of soluble salts, and lower amount of organic carbon, total phosphorus (Ptot) and total nitrogen (Ntot). Biocrust-forming mosses dominated lower and moister areas, characterized by gentler slopes and higher values of ATI with soils with higher contents of organic carbon, Ptot and Ntot. This study shows the potential to use UAVs to improve our understanding of drylands and to evaluate the control that the terrain has on biocrust distribution.
AB - Biocrusts (topsoil communities formed by mosses, lichens, bacteria, fungi, algae, and cyanobacteria) are a key biotic component of dryland ecosystems. Whilst climate patterns control the distribution of biocrusts in drylands worldwide, terrain and soil attributes can influence biocrust distribution at landscape scale. Multi-source unmanned aerial vehicle (UAV) imagery was used to map and study biocrust ecology in a typical dryland ecosystem in central Spain. Red, green and blue (RGB) imagery was processed using structure-from-motion techniques to map terrain attributes related to microclimate and terrain stability. Multispectral imagery was used to produce accurate maps (accuracy > 80%) of dryland ecosystem components (vegetation, bare soil and biocrust composition). Finally, thermal infrared (TIR) and multispectral imagery was used to calculate the apparent thermal inertia (ATI) of soil and to evaluate how ATI was related to soil moisture (r2 = 0.83). The relationship between soil properties and UAV-derived variables was first evaluated at the field plot level. Then, the maps obtained were used to explore the relationship between biocrusts and terrain attributes at ecosystem level through a redundancy analysis. The most significant variables that explain biocrust distribution are: ATI (34.4% of variance, F = 130.75; p < 0.001), Elevation (25.8%, F = 97.6; p < 0.001), and potential solar incoming radiation (PSIR) (52.9%, F = 200.1; p < 0.001). Differences were found between areas dominated by lichens and mosses. Lichen-dominated biocrusts were associated with areas with high slopes and low values of ATI, with soil characterized by a higher amount of soluble salts, and lower amount of organic carbon, total phosphorus (Ptot) and total nitrogen (Ntot). Biocrust-forming mosses dominated lower and moister areas, characterized by gentler slopes and higher values of ATI with soils with higher contents of organic carbon, Ptot and Ntot. This study shows the potential to use UAVs to improve our understanding of drylands and to evaluate the control that the terrain has on biocrust distribution.
KW - apparent inertia
KW - biocrusts
KW - biological soil crusts
KW - drylands
KW - lichen
KW - moss
KW - multispectral
KW - thermal
KW - UAV
UR - http://www.scopus.com/inward/record.url?scp=85112068424&partnerID=8YFLogxK
U2 - 10.1002/esp.5189
DO - 10.1002/esp.5189
M3 - Article
AN - SCOPUS:85112068424
SN - 0197-9337
VL - 46
SP - 2466
EP - 2484
JO - Earth Surface Processes and Landforms
JF - Earth Surface Processes and Landforms
IS - 12
ER -
