High-Resolution Snow-Covered Area Mapping in Forested Mountain Ecosystems Using PlanetScope Imagery
High-Resolution Snow-Covered Area Mapping in Forested Mountain Ecosystems Using PlanetScope Imagery
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DOI：
10.3390/rs14143409
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发表时间：
2022-07
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通讯作者：
Aji John;A. Cannistra;Kehan Yang;Amanda Tan;D. Shean;J. R. Lambers;N. Cristea
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作者：
Aji John;A. Cannistra;Kehan Yang;Amanda Tan;D. Shean;J. R. Lambers;N. Cristea
改善复杂森林地形中积雪覆盖地区的高分辨率（米级）测绘对于了解物种和水系统对气候变化的响应至关重要。 Planet Labs, Inc.（Planet，旧金山，加利福尼亚州，美国）的商业高分辨率图像可用于环境科学，因为它具有高空间（0.7–3.0 m）和时间（1–2 天）分辨率。使用传统辐射技术从行星图像中获取积雪覆盖的区域存在局限性，因为缺乏充分利用反射率差异来区分雪和云所需的短波红外波段。然而，最近的工作表明，仅使用 PlanetScope 4 波段（红、绿、蓝和近红外）反射率产品和基于卷积神经网络 (CNN) 的机器学习 (ML) 方法就可以成功绘制积雪面积 (SCA) 地图。为了评估附加功能如何改善现有模型性能，我们：(1) 在之前的工作基础上，使用附加输入数据（包括植被指标（归一化植被指数）和 DEM 衍生指标（高程、坡度和坡向））增强 CNN 模型，以改进森林和开放地形中的 SCA 制图，(2) 评估两个地理位置不同的地点（美国科罗拉多州冈尼森和瑞士恩加丁）的模型性能，以及 (3) 评估不同土地覆盖类型的模型性能。最佳增强模型使用归一化植被指数 (NDVI) 以及可见光（红色、绿色和蓝色）和近红外波段，F 分数为 0.89（冈尼森）和 0.93（恩加丁），并且发现比在甘尼森使用树冠高度和地形衍生测量值分别提高了 4% 和 2%。基于 NDVI 的模型不仅改进了原始仅波段模型检测森林积雪的能力，而且还改进了其他各种土地覆盖类型（间隙和树冠边缘）。我们使用三个森林冠层量化指标检查了模型在森林地区的性能，发现增强模型可以更好地识别冠层边缘和开放区域的积雪，但仍然低估了森林冠层下的积雪。虽然新功能比仅带选项提高了模型性能，但模型在识别茂密森林中树下的积雪方面仍然面临挑战，其性能随地理区域的变化而变化。森林环境中改进的高分辨率雪图可以支持涉及气候变化对山区生态系统影响的研究以及对以雪为主的河流流域的水文影响的评估。
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.