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 closed-canopy forests, not much is known about the spatial extent and distribution of scattered trees at a global scale. Due to the cost of high-resolution satellite imagery, global monitoring systems rely on medium-resolution satellites to monitor land use. Here we present a globally consistent method to identify trees with canopy diameters greater than three meters with medium-resolution optical and radar imagery. Biweekly cloud-free, pan-sharpened 10 meter Sentinel-2 optical imagery and Sentinel-1 radar imagery are used to train a fully convolutional network, consisting of a convolutional gated recurrent unit layer and a feature pyramid attention layer. Tested across more than 215,000 Sentinel-1 and Sentinel-2 pixels distributed from -60 to +60 latitude, the proposed model exceeds 75% user's and producer's accuracy identifying trees in hectares with a low to medium density (less than 40%) of tree cover, and 95% user's and producer's accuracy in hectares with dense (greater than 40%) tree cover. The proposed method increases the accuracy of monitoring tree presence in areas with sparse and scattered tree cover (less than 40%) by as much as 20%, and reduces commission and omission error in mountainous and very cloudy regions by nearly half. When applied across large, heterogeneous landscapes, the results demonstrate potential to map trees in high detail and accuracy over diverse landscapes across the globe. This information is important for understanding current land cover and can be used to detect changes in land cover such as agroforestry, buffer zones around biological hotspots, and expansion or encroachment of forests.
The Alan Turing Institute（艾伦·图灵研究所）；University College London（伦敦大学学院）
Information on urban tree canopies is fundamental to mitigating climate change [1] as well as improving quality of life [2]. Urban tree planting initiatives face a lack of up-to-date data about the horizontal and vertical dimensions of the tree canopy in cities. We present a pipeline that utilizes LiDAR data as ground-truth and then trains a multi-task machine learning model to generate reliable estimates of tree cover and canopy height in urban areas using multi-source multi-spectral satellite imagery for the case study of Chicago.
Phase-Preserving Trimodal Transformer for Tropical Forest Biomass Estimation Using Optical and PolInSAR Data
Institute of Computing (IComp), Federal University of Amazonas (UFAM)（亚马逊联邦大学计算研究所）；Brazilian Army Research Institute in the Amazon (IPEAM)（巴西陆军亚马逊研究所）；Military Institute of Engineering (IME)（军事工程学院）
Forest canopy height estimation from satellite RGB imagery using large-scale airborne LiDAR-derived training data and monocular depth estimation
Beijing Normal University（北京师范大学）；CSIRO Environment（英联邦科学与工业研究组织环境部）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Wuhan University（武汉大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）
A Deep Learning Approach to Estimate Canopy Height and Uncertainty by Integrating Seasonal Optical, SAR and Limited GEDI LiDAR Data over Northern Forests
High-resolution canopy height map in the Landes forest (France) based on GEDI, Sentinel-1, and Sentinel-2 data with a deep learning approach
Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-SCE32UVSQ, Université Paris Saclay（巴黎萨克雷大学气候与环境科学实验室）；Kayrros SAS（Kayrros SAS公司）；IGN, Laboratoire d’Inventaire Forestier（法国国家地理与森林信息研究所森林清查实验室）；INRAE, UMR TETIS, Université de Montpellier, AgroParisTech（法国国家农业食品与环境研究院、蒙彼利埃大学、巴黎高科农业学院）；Department of Geosciences and Natural Resource Management, University of Copenhagen（哥本哈根大学地球科学与自然资源管理系）；IGN, Service de l'Information Statistique, Forestière et Environnementale（法国国家地理与森林信息研究所统计、森林与环境信息服务处）；CESBIO, Université de Toulouse, CNES/CNRS/INRAE/IRD/UPS（图卢兹大学生物圈空间研究中心）；ISPA, UMR 1391, INRA Nouvelle-Aquitaine（法国国家农业研究院新阿基坦大区农业与环境相互作用研究所）
Airborne LiDAR point cloud representing a forest contains 3D data, from which vertical stand structure even of understory layers can be derived. This paper presents a tree segmentation approach for multi-story stands that stratifies the point cloud to canopy layers and segments individual tree crowns within each layer using a digital surface model based tree segmentation method. The novelty of the approach is the stratification procedure that separates the point cloud to an overstory and multiple understory tree canopy layers by analyzing vertical distributions of LiDAR points within overlappi…
Towards autonomous photogrammetric forest inventory using a lightweight under-canopy robotic drone
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, The National Land Survey of Finland（遥感与摄影测量系，芬兰地理空间研究 institute FGI，芬兰国家土地测绘局）
Airborne discrete return light detection and ranging (LiDAR) point clouds covering forested areas can be processed to segment individual trees and retrieve their morphological attributes. Segmenting individual trees in natural deciduous forests however remained a challenge because of the complex and multi-layered canopy. In this chapter, we present (i) a robust segmentation method that avoids a priori assumptions about the canopy structure, (ii) a vertical canopy stratification procedure that improves segmentation of understory trees, (iii) an occlusion model for estimating the point density o…
Manual Labelling Artificially Inflates Deep Learning-Based Segmentation Performance on RGB Images of Closed Canopy: Validation Using TLS
University of Cambridge（剑桥大学）；Queen Mary University of London（伦敦玛丽女王大学）；Digital Environment Research Institute, Queen Mary University of London（伦敦玛丽女王大学数字环境研究所）
ETH Zürich（苏黎世联邦理工学院）；University of Maryland（马里兰大学）；University of Zurich（苏黎世大学）
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 measure vertical forest structure predictive of aboveground biomass, the accurate interpretation of this vast amount of waveform data across the broad range of observational and environmental conditions is challenging. Here, we present a novel supervised machine learning approach to interpret GEDI waveforms and regress canopy top height globally. We propose a…
Characterizing the processes leading to deforestation is critical to the development and implementation of targeted forest conservation and management policies. In this work, we develop a deep learning model called ForestNet to classify the drivers of primary forest loss in Indonesia, a country with one of the highest deforestation rates in the world. Using satellite imagery, ForestNet identifies the direct drivers of deforestation in forest loss patches of any size. We curate a dataset of Landsat 8 satellite images of known forest loss events paired with driver annotations from expert interpr…
Physics informed Transformer-VAE for biophysical parameter estimation: PROSAIL model inversion in Sentinel-2 imagery
African Institute for Mathematical Sciences (AIMS), Accra, Ghana（非洲数学科学研究所（AIMS））；African Institute for Mathematical Sciences (AIMS), Cape Town, South Africa（非洲数学科学研究所（AIMS））
First Mapping the Canopy Height of Primeval Forests in the Tallest Tree Area of Asia
Beijing Forestry University（北京林业大学）；Delft University of Technology（代尔夫特理工大学）；University of Amsterdam（阿姆斯特丹大学）；Tibet Agricultural & Animal Husbandry University（西藏农牧学院）
A Novel Large Vision Foundation Model (LVFM)-based Approach for Generating High-Resolution Canopy Height Maps in Plantations for Precision Forestry Management
SilvaScenes: Tree Detection and Species Classification from Under-Canopy Images in Natural Forests
Northern Robotics Laboratory, Université Laval（北方机器人实验室，拉瓦尔大学）；Département des sciences du bois et de la forêt, Université Laval（林业与木材科学系，拉瓦尔大学）；Institut des Sciences de la Forêt tempérée, Université du Québec en Outaouais（温带森林科学研究所，魁北克outsouais大学）
Vision Transformers, a new approach for high-resolution and large-scale mapping of canopy heights
Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-SCE32UVSQ, Université Paris Saclay（巴黎萨克雷大学气候与环境科学实验室）；Kayrros SAS（Kayrros SAS公司）；ISPA, UMR 1391, INRA Nouvelle-Aquitaine（法国国家农业食品与环境研究院新阿基坦大区ISPA联合研究单位）；INRAE, UMR TETIS, Université de Montpellier, AgroParisTech（蒙彼利埃大学、巴黎高科农业学院、法国国家农业食品与环境研究院TETIS联合研究单位）；Department of Computer Science, École Normale Supérieure（巴黎高等师范学院计算机科学系）；Jet Propulsion Laboratory (JPL), California Institute of Technology（加州理工学院喷气推进实验室）；Université Paris-Saclay, AgroParisTech, INRAE, UMR 518 MIA Paris-Saclay（巴黎萨克雷大学、巴黎高科农业学院、法国国家农业食品与环境研究院MIA Paris-Saclay联合研究单位）
FORMSpoT: Revealing Fine-Scale Forest Disturbances from Nation-Wide 1.5 m Forest Canopy Height Time Series
organization= Laboratoire des Sciences du Climat et de l’Environnement (LSCE), CEA, CNRS, UVSQ, Université Paris-Saclay , city= Gif-sur-Yvette , country= France；organization= CNRS \& Département d'Informatique, École Normale Supérieure -- PSL , addressline= 45 Rue d'Ulm , postcode= 75005 , city= Paris , country= France；organization= EcoVision Lab, DM3L, University of Zurich , country= Switzerland；organization= LIGM, École des Ponts, CNRS, IPP, UGE；organization= Institute of Earth System Science
Technical University of Munich（慕尼黑工业大学）；ETH Zurich（苏黎世联邦理工学院）；Massachusetts Institute of Technology（麻省理工学院）
Forest carbon offsets are increasingly popular and can play a significant role in financing climate mitigation, forest conservation, and reforestation. Measuring how much carbon is stored in forests is, however, still largely done via expensive, time-consuming, and sometimes unaccountable field measurements. To overcome these limitations, many verification bodies are leveraging machine learning (ML) algorithms to estimate forest carbon from satellite or aerial imagery. Aerial imagery allows for tree species or family classification, which improves the satellite imagery-based forest type classi…
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 deforestation-free global supply chains is widely recognized, making progress in practice remains a challenge. Here, we propose an automated approach that aims to support conservation and sustainable land use planning decisions by mapping tropical landscapes at large scale and high spatial resolution following the High Carbon Stock (HCS) approach. A deep lear…
School of Geospatial Engineering and Science, Sun Yat-Sen University（中山大学测绘科学与技术学院）；Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University（香港理工大学土地测量及地理资讯学系）；Changjiang River Scientific Research Institute (CRSRI)（长江水利委员会长江科学院）；Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院数字地球重点实验室）；Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)（南方海洋科学与工程广东省实验室（珠海））
Registration of unmanned aerial vehicle laser scanning (ULS) and ground light detection and ranging (LiDAR) point clouds in forests is critical to create a detailed representation of a forest structure and an accurate inversion of forest parameters. However, forest occlusion poses challenges for marker-based registration methods, and some marker-free automated registration methods have low efficiency due to the process of object (e.g., tree, crown) segmentation. Therefore, we use a divide-and-conquer strategy and propose an automated and efficient method to register ULS and ground LiDAR point …
High Resolution Tree Height Mapping of the Amazon Forest using Planet NICFI Images and LiDAR-Informed U-Net Model
CTrees；Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）；Institute of Environment and Sustainability, University of California, Los Angeles（加州大学洛杉矶分校环境与可持续发展研究所）；Quapá Lab, Faculty of Architecture and Urbanism, University of São Paulo（圣保罗大学建筑与城市规划学院Quapá实验室）；Gamma Remote Sensing Ag（Gamma遥感公司）；USDA Forest Service, International Institute of Tropical Forestry（美国农业部林业局热带林业研究所）；EMBRAPA Satellite Monitoring（巴西农业研究院卫星监测中心）；Remote Sensing Division, National Institute for Space Research—INPE（巴西国家空间研究院遥感部）；Department of Geosciences and Natural Resource Management, University of Copenhagen（哥本哈根大学地球科学与自然资源管理系）；Laboratoire des Sciences du Climat et de l’Environnement, CEA-CNRS-UVSQ（气候与环境科学实验室（CEA-CNRS-UVSQ））
ETH Zurich（苏黎世联邦理工学院）；Norwegian Institute of Bioeconomy Research（挪威生物经济研究所）；University of Zurich（苏黎世大学）
Monitoring and managing Earth's forests in an informed manner is an important requirement for addressing challenges like biodiversity loss and climate change. While traditional in situ or aerial campaigns for forest assessments provide accurate data for analysis at regional level, scaling them to entire countries and beyond with high temporal resolution is hardly possible. In this work, we propose a method based on deep ensembles that densely estimates forest structure variables at country-scale with 10-meter resolution, using freely available satellite imagery as input. Our method jointly tra…
Highly Detailed and Generalizable Broadleaf Tree Crown Instance Segmentation from UAV Imagery
DeepForest Technologies Co., Ltd.（深森林技术有限公司）；YM Lab.（YM实验室）；Graduate School of Agriculture, Kyoto University（京都大学农业研究院）；Graduate School of Science, Osaka Metropolitan University（大阪 metropolitan 大学理学研究院）；Faculty of Tropical Forestry, Universiti Malaysia Sabah（马来西亚沙巴大学热带林业学院）；Forest Research Centre, Sabah Forestry Department（沙巴林业部门森林研究中心）
ETH Zürich（苏黎世联邦理工学院）；Yale University（耶鲁大学）；University of Zurich（苏黎世大学）
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 manage terrestrial ecosystems, mitigate climate change, and prevent biodiversity loss. Here, we present the first global, wall-to-wall canopy height map at 10 m ground sampling distance for the year 2020. No single data source meets these requirements: dedicated space missions like GEDI deliver sparse height data, with unprecedented coverage, whereas optical sate…
Understanding Representation Gaps Across Scales in Tropical Tree Species Classification from Drone Imagery
Mila – Quebec AI Institute（魁北克人工智能研究所）；McGill University（麦吉尔大学）；Université de Montréal（蒙特利尔大学）；Cary Institute of Ecosystem Studies（生态系统研究所）；Smithsonian Tropical Research Institute（史密松国家热带研究所）；Department of Plant Sciences, University of Cambridge（剑桥大学植物科学系）；Universidade do Estado do Mato Grosso (UNEMAT)（马托格罗索州立大学（UNEMAT））
Crossmodal learning for Crop Canopy Trait Estimation
Department of Computer Engineering, Iowa State University, Ames, IA, USA（计算机工程系，爱荷华州立大学）；Department of Mechanical Engineering, Iowa State University, Ames, IA, USA（机械工程系，爱荷华州立大学）
ETH Zurich（苏黎世联邦理工学院）；Massachusetts Institute of Technology（麻省理工学院）
International stakeholders increasingly invest in offsetting carbon emissions, for example, via issuing Payments for Ecosystem Services (PES) to forest conservation projects. Issuing trusted payments requires a transparent monitoring, reporting, and verification (MRV) process of the ecosystem services (e.g., carbon stored in forests). The current MRV process, however, is either too expensive (on-ground inspection of forest) or inaccurate (satellite). Recent works propose low-cost and accurate MRV via automatically determining forest carbon from drone imagery, collected by the landowners. The a…
ForestMamba: Sparse Mamba with Geometry-guided Queries for 3D Forest Point Cloud Segmentation
Nagoya University（名古屋大学）；RIKEN Seika（日本理化学研究所Seika研究中心）；University of California, Los Angeles（加州大学洛杉矶分校）；University of Twente（埃因霍温理工大学）；Ritsumeikan University（立命馆大学）
ECHOSAT: Estimating Canopy Height Over Space And Time
Department of Information Systems, University of Münster（慕尼黑大学信息系统系）；Laboratoire des Sciences du Climat et de l’Environnement (LSCE)（气候与环境科学实验室）；Zuse Institute Berlin (ZIB)（柏林Zuse研究所）；Jet Propulsion Laboratory (JPL), California Institute of Technology（喷气推进实验室（JPL），加州理工学院）；Technische Universität Berlin（柏林技术大学）
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术学院）；Ulm University（乌尔姆大学）
For many countries like Russia, Canada, or the USA, a robust and detailed tree species inventory is essential to manage their forests sustainably. Since one can not apply unmanned aerial vehicle (UAV) imagery-based approaches to large-scale forest inventory applications, the utilization of machine learning algorithms on satellite imagery is a rising topic of research. Although satellite imagery quality is relatively low, additional spectral channels provide a sufficient amount of information for tree crown classification tasks. Assuming that tree crowns are detected already, we use embeddings …
Estimating forest carbon stocks from high-resolution remote sensing imagery by reducing domain shift with style transfer
School of Geography and Remote Sensing, Guangzhou University（广州大学地理与遥感学院）；Innovation Center for Remote Sensing Big Data Intelligent Applications, Guangzhou University（广州大学遥感大数据智能应用创新中心）
