The 3D Elevation Program and energy for the Nation

Thatcher, Cindy A.; Lukas, Vicki; Stoker, Jason M.

doi:10.3133/fs20193051

Cited by 10 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These data were collected in 2017 at a standard matching those of data from 3DEP, a three-dimensional elevation program from the US Geological Survey. The goal of 3DEP is to acquire high-quality lidar data nationwide and make those resources publicly available by 2023 [41]. By using entirely public data, the study can be replicated in any study area with high-resolution lidar data or digital surface models (DSMs).…”

Section: Discussionmentioning

confidence: 99%

Sustainability at Auburn University: Assessing Rooftop Solar Energy Potential for Electricity Generation with Remote Sensing and GIS in a Southern US Campus

Stack

Narine

2022

Sustainability

View full text Add to dashboard Cite

Achieving sustainability through solar energy has become an increasingly accessible option in the United States (US). Nationwide, universities are at the forefront of energy efficiency and renewable generation goals. The aim of this study was to determine the suitability for the installation of photovoltaic (PV) systems based on their solar potential and corresponding electricity generation potential on a southern US university campus. Using Auburn University located in the southern US as a case study, freely available geospatial data were utilized, and geographic information system (GIS) approaches were applied to characterize solar potential across the 1875-acre campus. Airborne light detection and ranging (lidar) point clouds were processed to extract a digital surface model (DSM), from which slope and aspect were derived. The area and total solar radiation of campus buildings were calculated, and suitable buildings were then determined based on slope, aspect, and total solar radiation. Results highlighted that of 443 buildings, 323 were fit for solar arrays, and these selected rooftops can produce 27,068,555 kWh annually. This study demonstrated that Auburn University could benefit from rooftop solar arrays, and the proposed arrays would account for approximately 21.07% of annual electricity requirement by buildings, equivalent to 14.43% of total campus electricity for all operations. Given increasing open and free access to high-resolution lidar data across the US, methods from this study are adaptable to institutions nationwide, for the development of a comprehensive assessment of solar potential, toward meeting campus energy goals.

show abstract

Section: Discussionmentioning

confidence: 99%

Sustainability at Auburn University: Assessing Rooftop Solar Energy Potential for Electricity Generation with Remote Sensing and GIS in a Southern US Campus

Stack

Narine

2022

Sustainability

View full text Add to dashboard Cite

show abstract

“…For instance, the Shuttle Radar Topography Mission (SRTM) generated a near-global digital elevation model (DEM) at 30m resolution at the turn of the century (Farr and Kobrick 2000), and this enabled new insights into hydrography (Lehner, Verdin, and Jarvis 2008), cryology (Surazakov and Aizen 2006), vegetation remote sensing (Simard et al 2006), climate change-induced coastal flood risk (McGranahan, Balk, and Anderson 2007), limnology (NASA 2013) and more. But, what defines "high-resolution" changes over time, and a 30m DEM is considered moderate resolution today, relative to sub-meter topography data that are increasingly available and yield finer detail and thus new insights (Kruse, Baugh, and Perry 2015;Thatcher, Lukas, and Stoker 2020;C. Wang et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Ten simple rules for working with high resolution remote sensing data

Mahood¹,

Joseph²,

Spiers³

et al. 2023

Peer Community Journal

View full text Add to dashboard Cite

Researchers in Earth and environmental science can extract incredible value from highresolution (sub-meter, sub-hourly or hyper-spectral) remote sensing data, but these data can be difficult to use. Correct, appropriate and competent use of such data requires skills from remote sensing and the data sciences that are rarely taught together. In practice, many researchers teach themselves how to use high-resolution remote sensing data with ad hoc trial and error processes, often resulting in wasted effort and resources. In order to implement a consistent strategy, we outline ten rules with examples from Earth and environmental science to help academic researchers and professionals in industry work more effectively and competently with high-resolution data.

show abstract

“…Sample data were collected over the study site from OpenTopography (https://opentopography.org/ (accessed on 27 July 2022)), a web portal that facilitates open sharing and access to a wide variety of airborne lidar data worldwide. The data was acquired between 2016 and 2017 under the 3D Elevation Program (3DEP), which is providing the data through OpenTopography [28]. The ready-classified airborne lidar data had an average point density of 9.44 pts per square meter, georeferenced to NAD83(2011), UTM zone 15N reference frame.…”

mentioning

confidence: 99%

Landsat-Scale Regional Forest Canopy Height Mapping Using ICESat-2 Along-Track Heights: Case Study of Eastern Texas

Malambo

Popescu

2022

Remote Sensing

View full text Add to dashboard Cite

Spaceborne profiling lidar missions such as the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) are collecting direct elevation measurements, supporting the retrieval of vegetation attributes such as canopy height that are crucial in forest carbon and ecological studies. However, such profiling lidar systems collect observations along predefined ground tracks which limit the spatially complete mapping of forest canopy height. We demonstrate that the fusion of ICESat-2 along-track canopy height estimates and ancillary Landsat and LANDFIRE (Landscape Fire and Resource Management Planning Tools Project) data can enable the generation of spatially complete canopy height data at a regional level in the United States. We developed gradient-boosted regression models relating canopy heights with ancillary data values and used them to predict canopy height in unobserved locations at a 30 m spatial resolution. Model performance varied (R2 = 0.44 − 0.50, MAE = 2.61–2.80 m) when individual (per month) Landsat data and LANDFIRE data were used. Improved performance was observed when combined Landsat and LANDFIRE data were used (R2 = 0.69, MAE = 2.09 m). We produced a gridded canopy height product over our study area in eastern Texas, which agreed moderately (R2 = 0.46, MAE = 4.38 m) with independent airborne lidar-derived canopy heights. Further, we conducted a comparative assessment with the Global Forest Canopy Height product, an existing 30 m spatial resolution canopy height product generated using GEDI (Global Ecosystem Dynamics Investigation) canopy height and multitemporal Landsat data. In general, our product showed better agreement with airborne lidar heights than the global dataset (R2 = 0.19 MAE = 5.83 m). Major differences in canopy height values between the two products are attributed to land cover changes, height metrics used (98th in this study vs 95th percentile), and the inherent differences in lidar sampling and their geolocation uncertainties between ICESat-2 and GEDI. On the whole, our integration of ICESat-2 data with ancillary datasets was effective for spatially complete canopy height mapping. For better modeling performance, we recommend the careful selection of ICESat-2 datasets to remove erroneous data and applying a series of Landsat data to account for phenological changes. The canopy height product provides a valuable spatially detailed and synoptic view of canopy heights over the study area, which would support various forestry and ecological assessments at an enhanced 30 Landsat spatial resolution.

show abstract

The 3D Elevation Program and energy for the Nation

Cited by 10 publications

References 0 publications

Sustainability at Auburn University: Assessing Rooftop Solar Energy Potential for Electricity Generation with Remote Sensing and GIS in a Southern US Campus

Sustainability at Auburn University: Assessing Rooftop Solar Energy Potential for Electricity Generation with Remote Sensing and GIS in a Southern US Campus

Ten simple rules for working with high resolution remote sensing data

Landsat-Scale Regional Forest Canopy Height Mapping Using ICESat-2 Along-Track Heights: Case Study of Eastern Texas

Contact Info

Product

Resources

About