Remotely Sensed Spatial Structure as an Indicator of Internal Changes of Vegetation Communities in Desert Landscapes

Abstract

Desert environments are sensitive to disturbances, and their functions and processes can take many years to recover. Detecting early signs of disturbance is critical, but developing such a capability for expansive remote desert regions is challenging. Using a variogram and 15-cm resolution Visible Atmospherically Resistant Index (VARI) imagery, we examined the usefulness of the spatial structure of desert lands for monitoring early signs of habitat changes using the Riverside East solar energy zone located within Riverside County, California. We tested the method on four habitat types in the region, Parkinsonia florida–Olneya tesota, Chorizanthe rigida–Geraea canescens, Larrea tridentata–Ambrosia dumosa, and Larrea tridentata–Encelia farinosa alliances. The results showed that the sill, range, form, and partial sill of the variogram generated from VARI strongly correlate with overall vegetation cover, average canopy size, canopy size variation, and spatial structure within a dryland habitat, respectively. Establishing a baseline of variogram parameters for each habitat and comparing to subsequent monitoring parameters would be most effective for detecting internal changes because values of variogram parameters would not match absolute values of landscape properties. When monitoring habitats across varying landscape characteristics, a single appropriate image resolution would likely be the resolution that could adequately characterize the habitat dominated by the smallest vegetation. For the variogram generated from VARI, which correlates to vegetation greenness, the sills may indicate the health of vegetation communities. However, further studies are warranted to determine the effectiveness of variograms for monitoring habitat health. Remotely sensed landscape structure obtained from variograms could provide complementary information to traditional methods for monitoring internal changes in dryland vegetation communities.

Assessing the effectiveness of ground truth data to capture landscape variability from an agricultural region using Gaussian simulation and geostatistical techniques

“…Variograms have been widely used to characterize the spatial variations inherent in remote sensing images (Woodcock et al, 1988;Wallace et al, 2000;Hamada et al, 2019). Our variogram results implied that the set of GT samples was sufficient and reliable, and was capable of capturing the spatial variance components of field-level sampling using image indices.…”

Review of Desert Mobility Assessment and Desertification Monitoring Based on Remote Sensing

“…The spatial structure of a desert can predict the signs of desert change. In order to verify this, Hamada et al [121] used the variogram function generated by the VARI to provide theoretical support for the correlation between them. Furthermore, LST maps can reflect desert characteristics to a certain extent [122][123][124].…”

Assessing the Consistency of Estimated Ground Cover Fractions between the BLM AIM Method and Optical Remote Sensing Method

“…Since 2012, Argonne National Laboratory (Argonne) has supported the Bureau of Land Management (BLM) in developing remote sensing methodologies for long-term environmental monitoring of Palo Verde Mesa in eastern Riverside County, California, including methods for: detailed mapping of ephemeral streams Grippo 2015a, Hamada et al 2016), estimating fractional cover of desert-land surface components (e.g., trees, shrubs, litters, and bare ground) (Hamada and Grippo 2015a), evaluating erosion risk or land stability Grippo 2015a, 2015b), and characterizing vegetation alliances using spatial structure and geostatistical approaches (Hamada et al 2019). These studies showed the promise of remote sensing for monitoring changes in desert landscapes by providing information that would be difficult to obtain through field surveys.…”