Remote-sensing application for facilitating land resource assessment and monitoring for utility-scale solar energy development

Abstract

Abstract. A monitoring plan that incorporates regional datasets and integrates cost-effective data collection methods is necessary to sustain the long-term environmental monitoring of utility-scale solar energy developments in expansive, environmentally sensitive desert regions. An image processing routine using very high spatial resolution (VHSR; i.e., 15 cm) multispectral imagery collected in November 2012 and January 2014 was developed to characterize ephemeral streams, vegetation, and land surfaces in the southwestern United States, where increased utilityscale solar development is anticipated. In addition to information about desert landscapes, the methodology integrates existing spectral indices and transformations (e.g., visible atmospherically resistant index and principal components); a new index called the erosion resistance index (ERI); and digital terrain and surface models, all of which were derived from a common VHSR image. The methodology identified fine-scale ephemeral streams at greater detail than does the National Hydrography Dataset, and it accurately estimated the distribution of vegetation and the fractional cover of various surface types. The ERI classified surface types that have a range of erosive potentials. The remote-sensing methodology could ultimately reduce uncertainty and monitoring costs for all stakeholders by providing a cost-effective monitoring approach that accurately characterizes the land resources at potential development sites. © The Authors.Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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

“…The Visible Atmospherically Resistant Index (VARI, Gitelson et al 2002) was computed using the spectrally standardized NAIP image. This index was found to be the most effective for characterizing vegetation in the area in our previous study (Hamada and Grippo 2015a). The original 4-band image and VARI layer were stacked and then clipped to the simulated AIM field survey plots (i.e., a 30-m buffer around each AIM survey point).…”

“…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.…”

Modeling photovoltaic solar farm site suitability using a multi-criteria evaluation in Southern Ontario, Canada

“…Most studies locating optimal solar farm sites focus on warm, sunny climates, such as Turkey (Uyan, 2013), Australia (Law et al, 2014), California (Hamada and Grippo, 2015), Oman (Charabi and Gastli, 2011), and India (Mahtta et al, 2014). These areas tend to have more solar energy potential than areas with more variable seasons, cloud coverage, and less solar irradiance (NASA Surface Meteorology and Solar Energy, 2008).…”

Mapping South America’s Drylands through Remote Sensing—A Review of the Methodological Trends and Current Challenges

“…In the scope of RS, how can satellite sensors support land assessment and monitoring for solar energy development in the drylands of South America? A research study conducted in drylands of the United States [127] successfully mapped solar energy potential using very high-resolution imagery and multiple vegetation indices (e.g., MSAVI and NDVI).…”