Improving high-resolution (meter-scale) mapping of snow-covered areas in complex and forested terrains is critical to understanding the responses of species and water systems to climate change. Commercial high-resolution imagery from Planet Labs, Inc. (Planet, San Francisco, CA, USA) can be used in environmental science, as it has both high spatial (0.7–3.0 m) and temporal (1–2 day) resolution. Deriving snow-covered areas from Planet imagery using traditional radiometric techniques have limitations due to the lack of a shortwave infrared band that is needed to fully exploit the difference in reflectance to discriminate between snow and clouds. However, recent work demonstrated that snow cover area (SCA) can be successfully mapped using only the PlanetScope 4-band (Red, Green, Blue and NIR) reflectance products and a machine learning (ML) approach based on convolutional neural networks (CNN). To evaluate how additional features improve the existing model performance, we: (1) build on previous work to augment a CNN model with additional input data including vegetation metrics (Normalized Difference Vegetation Index) and DEM-derived metrics (elevation, slope and aspect) to improve SCA mapping in forested and open terrain, (2) evaluate the model performance at two geographically diverse sites (Gunnison, Colorado, USA and Engadin, Switzerland), and (3) evaluate the model performance over different land-cover types. The best augmented model used the Normalized Difference Vegetation Index (NDVI) along with visible (red, green, and blue) and NIR bands, with an F-score of 0.89 (Gunnison) and 0.93 (Engadin) and was found to be 4% and 2% better than when using canopy height- and terrain-derived measures at Gunnison, respectively. The NDVI-based model improves not only upon the original band-only model’s ability to detect snow in forests, but also across other various land-cover types (gaps and canopy edges). We examined the model’s performance in forested areas using three forest canopy quantification metrics and found that augmented models can better identify snow in canopy edges and open areas but still underpredict snow cover under forest canopies. While the new features improve model performance over band-only options, the models still have challenges identifying the snow under trees in dense forests, with performance varying as a function of the geographic area. The improved high-resolution snow maps in forested environments can support studies involving climate change effects on mountain ecosystems and evaluations of hydrological impacts in snow-dominated river basins.
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Performance evaluation of three DEM‐based fluvial terrace mapping methods
The availability of high‐resolution digital elevation models (DEMs) derived from airborne light detection and ranging (LiDAR) surveys has spurred the development of several methods to identify and map fluvial terraces. The post‐glacial landscape of the Sheepscot River watershed, Maine, where land‐use change has produced fill terraces upstream of historic dam sites, was selected to implement a comparison between terrace mapping methodologies. At four study sites within the watershed, terraces were manually mapped on LiDAR‐DEM‐derived hillshade images to facilitate the comparison among fully and semi‐automated DEM‐based procedures, including: (1) spatial relationships between interpreted terraces and surrounding natural topography, (2) feature classification algorithms, and (3) the TerEx terrace mapping toolbox. Each method was evaluated based on its accuracy and ease of implementation. The four study sites have varying longitudinal slope (0.0008–0.006 m/m), channel width (< 5–30 m), surrounding landscape relief (20–80 m), type and density of surrounding land use, and mapped surficial geologic units. All methods generally overestimate terrace areas (average predicted area 210% of manually defined area) with the most accurate results achieved within confined river valleys surrounded by the steep hillslopes. Accuracy generally decreases for study sites surrounded by low‐relief landscapes (predicted areas ranged 4–953% of manual delineations). We conclude with the advantages and drawbacks of each method tested and make recommendations for the scenarios where the use of each method is most appropriate. Copyright © 2016 John Wiley & Sons, Ltd.
more » « less- NSF-PAR ID:
- 10237623
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Earth Surface Processes and Landforms
- Volume:
- 41
- Issue:
- 8
- ISSN:
- 0197-9337
- Format(s):
- Medium: X Size: p. 1144-1152
- Size(s):
- ["p. 1144-1152"]
- Sponsoring Org:
- National Science Foundation
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