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相关论文: Very high resolution canopy height maps from RGB i…

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With the rise in global greenhouse gas emissions, accurate large-scale tree canopy height maps are essential for understanding forest structure, estimating above-ground biomass, and monitoring ecological disruptions. To this end, we present…

机器学习 · 计算机科学 2026-03-13 Jan Pauls , Max Zimmer , Berkant Turan , Sassan Saatchi , Philippe Ciais , Sebastian Pokutta , Fabian Gieseke

The worldwide variation in vegetation height is fundamental to the global carbon cycle and central to the functioning of ecosystems and their biodiversity. Geospatially explicit and, ideally, highly resolved information is required to…

计算机视觉与模式识别 · 计算机科学 2022-04-19 Nico Lang , Walter Jetz , Konrad Schindler , Jan Dirk Wegner

Accurate and timely monitoring of forest canopy heights is critical for assessing forest dynamics, biodiversity, carbon sequestration as well as forest degradation and deforestation. Recent advances in deep learning techniques, coupled with…

The increasing demand for commodities is leading to changes in land use worldwide. In the tropics, deforestation, which causes high carbon emissions and threatens biodiversity, is often linked to agricultural expansion. While the need for…

计算机视觉与模式识别 · 计算机科学 2021-07-16 Nico Lang , Konrad Schindler , Jan Dirk Wegner

Tree canopy height is one of the most important indicators of forest biomass, productivity, and species diversity, but it is challenging to measure accurately from the ground and from space. Here, we used a U-Net model adapted for…

We propose a framework for global-scale canopy height estimation based on satellite data. Our model leverages advanced data preprocessing techniques, resorts to a novel loss function designed to counter geolocation inaccuracies inherent in…

计算机视觉与模式识别 · 计算机科学 2026-03-13 Jan Pauls , Max Zimmer , Una M. Kelly , Martin Schwartz , Sassan Saatchi , Philippe Ciais , Sebastian Pokutta , Martin Brandt , Fabian Gieseke

Forest monitoring is critical for climate change mitigation. However, existing global tree height maps provide only static snapshots and do not capture temporal forest dynamics, which are essential for accurate carbon accounting. We…

Estimating canopy height and its changes at meter resolution from satellite imagery is a significant challenge in computer vision with critical environmental applications. However, the lack of open-access datasets at this resolution hinders…

Accurate forest canopy height estimation is essential for evaluating aboveground biomass and carbon stock dynamics, supporting ecosystem monitoring services like timber provisioning, climate change mitigation, and biodiversity conservation.…

计算机视觉与模式识别 · 计算机科学 2024-10-25 Jose B. Castro , Cheryl Rogers , Camile Sothe , Dominic Cyr , Alemu Gonsamo

Large-scale, high-resolution forest canopy height mapping plays a crucial role in understanding regional and global carbon and water cycles. Spaceborne LiDAR missions, including the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) and…

计算机视觉与模式识别 · 计算机科学 2026-02-10 Yongkang Lai , Xihan Mu , Dasheng Fan , Donghui Xie , Shanxin Guo , Wenli Huang , Tianjie Zhao , Guangjian Yan

Tree canopy height is one of the most important indicators of forest biomass, productivity, and ecosystem structure, but it is challenging to measure accurately from the ground and from space. Here, we used a U-Net model adapted for…

In intensively managed forests in Europe, where forests are divided into stands of small size and may show heterogeneity within stands, a high spatial resolution (10 - 20 meters) is arguably needed to capture the differences in canopy…

Accurate canopy height information is essential for quantifying forest carbon, monitoring restoration and degradation, and assessing habitat structure, yet high-fidelity measurements from airborne laser scanning (ALS) remain unevenly…

Mapping forest resources and carbon is important for improving forest management and meeting the objectives of storing carbon and preserving the environment. Spaceborne remote sensing approaches have considerable potential to support forest…

机器学习 · 统计学 2023-11-10 David Morin , Milena Planells , Stéphane Mermoz , Florian Mouret

High-resolution mapping of canopy height is essential for forest management and biodiversity monitoring. Although recent studies have led to the advent of deep learning methods using satellite imagery to predict height maps, these…

Scattered trees outside of dense, closed-canopy forests are very important for carbon sequestration, supporting livelihoods, maintaining ecosystem integrity, and climate change adaptation and mitigation. In contrast to trees inside of…

计算机视觉与模式识别 · 计算机科学 2021-02-03 John Brandt , Fred Stolle

We have developed the world's first canopy height map of the distribution area of world-level giant trees. This mapping is crucial for discovering more individual and community world-level giant trees, and for analyzing and quantifying the…

计算机视觉与模式识别 · 计算机科学 2024-04-24 Guangpeng Fan , Fei Yan , Xiangquan Zeng , Qingtao Xu , Ruoyoulan Wang , Binghong Zhang , Jialing Zhou , Liangliang Nan , Jinhu Wang , Zhiwei Zhang , Jia Wang

Estimating global tree canopy height is crucial for forest conservation and climate change applications. However, capturing high-resolution ground truth canopy height using LiDAR is expensive and not available globally. An efficient…

计算机视觉与模式识别 · 计算机科学 2025-05-27 Daniele Rege Cambrin , Isaac Corley , Paolo Garza

NASA's Global Ecosystem Dynamics Investigation (GEDI) is a key climate mission whose goal is to advance our understanding of the role of forests in the global carbon cycle. While GEDI is the first space-based LIDAR explicitly optimized to…

机器学习 · 计算机科学 2021-11-05 Nico Lang , Nikolai Kalischek , John Armston , Konrad Schindler , Ralph Dubayah , Jan Dirk Wegner

Fine-scale forest monitoring is essential for understanding canopy structure and its dynamics, which are key indicators of carbon stocks, biodiversity, and forest health. Deep learning is particularly effective for this task, as it…

计算机视觉与模式识别 · 计算机科学 2026-05-28 Ekaterina Kalinicheva , Florian Helen , Stéphane Mermoz , Florian Mouret , Milena Planells
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