An examination of GNSS positioning under dense conifer forest canopy in the Pacific Northwest, USA

IF 4.5 Q2 ENVIRONMENTAL SCIENCES Remote Sensing Applications-Society and Environment Pub Date : 2025-01-01 Epub Date: 2024-12-14 DOI:10.1016/j.rsase.2024.101428
Jacob L. Strunk , Stephen E. Reutebuch , Robert J. McGaughey , Hans-Erik Andersen
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Abstract

Accurate positioning in the forest (e.g., less than 1–2 m horizontal error) is needed to leverage the potential of high-resolution auxiliary data sources such as airborne or satellite imagery, lidar, and photogrammetric heights used in forest monitoring. Unfortunately, typical short duration occupations in the forest with budget Global Navigation Satellite System (GNSS; GPS is the American constellation) receivers are generally inaccurate (horizontal errors >5–20 m). This study demonstrates that accurate GNSS positioning is feasible beneath 40 to 60 m-tall closed-canopy conifer forests of western Washington state, USA by using survey-grade receivers with at least 15-min occupations. We also demonstrate the effects of receiver height, occupation duration, base-station distance, and differential post-processing modes (e.g., autonomous, code, fixed-integer, and floating-point) on horizontal positioning accuracies in the forest.
A geodetic survey was our benchmark for accuracy estimation but is difficult to replicate by most other GNSS users in the forest. The difficulty in setting up a geodetic survey has led to common usage of naïve accuracy estimators based on within-occupation coordinate variation (e.g., the “accuracy” reported on the face of a handheld GNSS device). In this study we demonstrate the efficacy of two simple alternatives that outperform the naïve estimator; the naïve esimator was shown to perform poorly.
The findings in this study on GNSS performance and positioning accuracy estimation supports more effective use of GNSS technology in applications that require high-performance GNSS positioning in the forest.
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美国太平洋西北部浓密针叶林冠层下GNSS定位研究
森林中的精确定位(例如,水平误差小于1-2米)需要利用高分辨率辅助数据源的潜力,如机载或卫星图像、激光雷达和森林监测中使用的摄影测量高度。不幸的是,森林中典型的短期职业与预算全球导航卫星系统(GNSS);GPS(美国星座)接收器通常不准确(水平误差为5-20米)。本研究表明,在美国华盛顿州西部40 - 60米高的封闭冠层针叶林下,使用测量级接收器进行至少15分钟的定位是可行的。我们还展示了接收器高度、占用时间、基站距离和不同的后处理模式(如自主、编码、固定整数和浮点)对森林中水平定位精度的影响。大地测量是我们精度估计的基准,但很难被森林中的大多数其他GNSS用户复制。建立大地测量的困难导致普遍使用基于职业内坐标变化的naïve精度估计器(例如,手持式GNSS设备表面报告的“精度”)。在本研究中,我们证明了两种简单的替代方案的有效性,它们优于naïve估计器;结果表明,naïve仿真器表现不佳。这项关于GNSS性能和定位精度估计的研究结果,为在森林中需要高性能GNSS定位的应用中更有效地使用GNSS技术提供了支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
8.00
自引率
8.50%
发文量
204
审稿时长
65 days
期刊介绍: The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems
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