Long-Term Monitoring of Utility-Scale Solar Energy Development and Application of Remote Sensing Technologies: Summary Report

Hamada, Yuki; Grippo, Mark A.; Smith, Karen P.

doi:10.2172/1172035

Cited by 3 publications

(22 citation statements)

References 16 publications

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“…Using the VHSR image frames, a digital surface model (DSM) and digital terrain model (DTM) were generated by the vendor, who used an automated aerial triangulation method. The DSM has a spatial resolution of 71.5 cm, and the DTM has one of 3.8 m. 10 Field data were collected on September 25 and 26, 2012, and January 21-23, 2014. Occurrences of vegetation and bare ground (e.g., exposed soil and desert pavement) and plant life-form types were recorded.…”

Section: Datamentioning

confidence: 99%

“…Occurrences of vegetation and bare ground (e.g., exposed soil and desert pavement) and plant life-form types were recorded. 10 Based on the information from the field survey, additional locations of vegetation, exposed soil, and desert pavement were manually digitized in the VHSR images in order to generate supplemental reference data for training and accuracy assessment. This image-interpretation approach was taken because the amount of data collected during the field work was restricted due to time constraints and inclement weather.…”

Section: Datamentioning

confidence: 99%

“…The accuracy of the vegetation maps was assessed using >2000 vegetation canopies and patches in three randomly selected areas that were independent from the previous nine areas, and the optimal SVI was selected. 10 The optimal SVI layer was combined with the original VHSR image mosaic, and iterative self-organizing classification 21 was performed to stratify the stacked image into 50 classes based on the similarity in pixel values across layers. Among the resultant 50 classes, five ecologically/environmentally meaningful and spatially cohesive classes (or surface types)-green vegetation, nonphotosynthetic vegetation (NPV), light-colored soil, dark-colored soil, and desert pavement-were identified based on the comparison with the VHSR image mosaic and field data.…”

Section: Extracting Vegetation Features and Estimating Fractional Covermentioning

confidence: 99%

“…The fractional cover of each surface type was computed for a 15 m × 15 m area using the surface-type map. 10 …”

Section: Extracting Vegetation Features and Estimating Fractional Covermentioning

confidence: 99%

“…Channel centerlines were extracted from the raster classification map by identifying edges of Thiessen polygons that corresponded to channels, and irrelevant lines connecting to the centerline were removed to finalize the ephemeral stream centerline map. 10 Both ephemeral channel classification and centerline maps were examined to evaluate how well the algorithm extracts stream channels in desert landscapes. The channel classification map was first qualitatively examined with regard to the overall abundance and spatial patterns of the extracted channels by comparing it with the National Hydrography Dataset (NHD).…”

Section: Extracting Ephemeral Streamsmentioning

confidence: 99%

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

Hamada

Grippo

2015

J. Appl. Remote Sens

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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.

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Section: Datamentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Extracting Vegetation Features and Estimating Fractional Covermentioning

confidence: 99%

“…The fractional cover of each surface type was computed for a 15 m × 15 m area using the surface-type map. 10 …”

Section: Extracting Vegetation Features and Estimating Fractional Covermentioning

confidence: 99%

Section: Extracting Ephemeral Streamsmentioning

confidence: 99%

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

Hamada

Grippo

2015

J. Appl. Remote Sens

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Long-Term Monitoring of Desert Land and Natural Resources and Application of Remote Sensing Technologies

Hamada

Rollins

2016

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

et al. 2019

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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.

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Long-Term Monitoring of Utility-Scale Solar Energy Development and Application of Remote Sensing Technologies: Summary Report

Cited by 3 publications

References 16 publications

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

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

Long-Term Monitoring of Desert Land and Natural Resources and Application of Remote Sensing Technologies

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

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