TY - JOUR
T1 - A long-term spatiotemporal analysis of biocrusts across a diverse arid environment
T2 - The case of the Israeli-Egyptian sandfield
AU - Noy, Klil
AU - Ohana-Levi, Noa
AU - Panov, Natalya
AU - Silver, Micha
AU - Karnieli, Arnon
N1 - Funding Information:
This project has received funding from the European Union's Horizon 2020 research and innovation program “European Long-Term Ecosystem, Critical Zone and Socio-Ecological systems Research Infrastructure PLUS” (eLTER PLUS) under grant agreement no. 871128 . The authors wish to thank Mr. Amiram Cohen and Ms. Shir Triki from the Israel Nature and Parks Authority (NPA) for their valuable insights, collaboration, and information sharing. The authors would also like to thank Dr. Yael Friedman-Levi for her infographical insights and suggestions. The authors greatly appreciate the support and assistance provided by Dr. Timea Ignat.
Publisher Copyright:
© 2021 The Authors
PY - 2021/6/20
Y1 - 2021/6/20
N2 - Spatiotemporal data can be analyzed using spatial, time-series, and machine learning algorithms to extract regional biocrust trends. Analyzing the spatial trends of biocrusts through time, using satellite imagery, may improve the quantification and understanding of their change drivers. The current work strives to develop a unique framework for analyzing spatiotemporal trends of the spectral Crust Index (CI), thus identifying the drivers of the biocrusts' spatial and temporal patterns. To fulfill this goal, CI maps, derived from 31 annual Landsat images, were analyzed by applying advanced statistical and machine learning algorithms. A comprehensive overview of biocrusts' spatiotemporal patterns was achieved using an integrative approach, including a long-term analysis, using the Mann-Kendall (MK) statistical test, and a short-term analysis, using a rolling MK with a window size of five years. Additionally, temporal clustering, using the partition around medoids (PAM) algorithm, was applied to model the spatial multi-annual dynamics of the CI. A Granger Causality test was then applied to quantify the relations between CI dynamics and precipitation. The findings show that 88.7% of pixels experienced a significant negative change, and only 0.5% experienced a significant positive change. A strong association was found in temporal trends among all clusters (0.67 ≤ r ≤ 0.8), signifying a regional effect due to precipitation levels (p < 0.05 for most clusters). The biocrust dynamics were also locally affected by anthropogenic factors (0.58 > CI > 0.64 and 0.64 > CI > 0.71 for strongly and weakly affected regions, respectively). A spatiotemporal analysis of a series of spaceborne images may improve conservation management by evaluating biocrust development in drylands. The suggested framework may also by applied to various disciplines related to quantifying spatial and temporal trends.
AB - Spatiotemporal data can be analyzed using spatial, time-series, and machine learning algorithms to extract regional biocrust trends. Analyzing the spatial trends of biocrusts through time, using satellite imagery, may improve the quantification and understanding of their change drivers. The current work strives to develop a unique framework for analyzing spatiotemporal trends of the spectral Crust Index (CI), thus identifying the drivers of the biocrusts' spatial and temporal patterns. To fulfill this goal, CI maps, derived from 31 annual Landsat images, were analyzed by applying advanced statistical and machine learning algorithms. A comprehensive overview of biocrusts' spatiotemporal patterns was achieved using an integrative approach, including a long-term analysis, using the Mann-Kendall (MK) statistical test, and a short-term analysis, using a rolling MK with a window size of five years. Additionally, temporal clustering, using the partition around medoids (PAM) algorithm, was applied to model the spatial multi-annual dynamics of the CI. A Granger Causality test was then applied to quantify the relations between CI dynamics and precipitation. The findings show that 88.7% of pixels experienced a significant negative change, and only 0.5% experienced a significant positive change. A strong association was found in temporal trends among all clusters (0.67 ≤ r ≤ 0.8), signifying a regional effect due to precipitation levels (p < 0.05 for most clusters). The biocrust dynamics were also locally affected by anthropogenic factors (0.58 > CI > 0.64 and 0.64 > CI > 0.71 for strongly and weakly affected regions, respectively). A spatiotemporal analysis of a series of spaceborne images may improve conservation management by evaluating biocrust development in drylands. The suggested framework may also by applied to various disciplines related to quantifying spatial and temporal trends.
KW - Crust Index
KW - Landsat
KW - Long-term trend
KW - Remote sensing
KW - Time-series clustering
UR - http://www.scopus.com/inward/record.url?scp=85101340613&partnerID=8YFLogxK
U2 - 10.1016/j.scitotenv.2021.145154
DO - 10.1016/j.scitotenv.2021.145154
M3 - Article
C2 - 33609826
AN - SCOPUS:85101340613
VL - 774
JO - Science of the Total Environment
JF - Science of the Total Environment
SN - 0048-9697
M1 - 145154
ER -