A long-term spatiotemporal analysis of biocrusts across a diverse arid environment: The case of the Israeli-Egyptian sandfield

Klil Noy; Noa Ohana-Levi; Natalya Panov; Micha Silver; Arnon Karnieli

doi:10.1016/j.scitotenv.2021.145154

A long-term spatiotemporal analysis of biocrusts across a diverse arid environment: The case of the Israeli-Egyptian sandfield

Sci Total Environ. 2021 Jun 20:774:145154. doi: 10.1016/j.scitotenv.2021.145154. Epub 2021 Feb 6.

Authors

Klil Noy¹, Noa Ohana-Levi², Natalya Panov¹, Micha Silver¹, Arnon Karnieli³

Affiliations

¹ The Remote Sensing Laboratory, French Associates Institute for Agriculture and Biotechnology of Drylands, the Jacob Blaustein Institutes for Desert Research, Ben-Gurion University, Sede Boker Campus, 84990, Israel.
² The Remote Sensing Laboratory, French Associates Institute for Agriculture and Biotechnology of Drylands, the Jacob Blaustein Institutes for Desert Research, Ben-Gurion University, Sede Boker Campus, 84990, Israel; Independent researcher, Ashalim, 85512, Israel.
³ The Remote Sensing Laboratory, French Associates Institute for Agriculture and Biotechnology of Drylands, the Jacob Blaustein Institutes for Desert Research, Ben-Gurion University, Sede Boker Campus, 84990, Israel. Electronic address: karnieli@bgu.ac.il.

PMID: 33609826
DOI: 10.1016/j.scitotenv.2021.145154

Abstract

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.

Keywords: Crust Index; Landsat; Long-term trend; Remote sensing; Time-series clustering.

Publication types

Review

MeSH terms

Egypt
Satellite Imagery*
Spatio-Temporal Analysis