Quantification of forest biomass stocks and their dynamics is important for implementing effective climate change mitigation measures. The knowledge is needed, e.g., for local forest management, studying the processes driving af-, re-, and deforestation, and can improve the accuracy of carbon-accounting. Remote sensing using airborne LiDAR can be used to perform these measurements of vegetation structure at large scale. We present deep learning systems for predicting wood volume, above-ground biomass (AGB), and subsequently above-ground carbon stocks directly from airborne LiDAR point clouds. We devise different neural network architectures for point cloud regression and evaluate them on remote sensing data of areas for which AGB estimates have been obtained from field measurements in the Danish national forest inventory. Our adaptation of Minkowski convolutional neural networks for regression gave the best results. The deep neural networks produced significantly more accurate wood volume, AGB, and carbon stock estimates compared to state-of-the-art approaches operating on basic statistics of the point clouds. In contrast to other methods, the proposed deep learning approach does not require a digital terrain model. We expect this finding to have a strong impact on LiDAR-based analyses of biomass dynamics.
Minkowski-MambaNet: A Point Cloud Framework with Selective State Space Models for Forest Biomass Quantification
School of Computer Science and Technology, the Key Laboratory of Signal Detection and Processing, and the Xinjiang Multimodal Intelligent Processing and Information Security Engineering Technology Research Center, Xinjiang University（计算机科学与技术学院、信号检测与处理重点实验室以及新疆多模态智能处理与信息安全工程技术研究中心，新疆大学）；National Key Laboratory for Novel Software Technology, Nanjing University（新型软件技术国家实验室，南京大学）；Xinjiang Guanghui New Energy Co., Ltd（新疆广汇新能源有限公司）；Hongyousoft Co., Ltd（宏远软件有限公司）
The Hong Kong Polytechnic University（香港理工大学）；The Hong Kong Polytechnic University Shenzhen Research Institute（香港理工大学深圳研究院）；Nanjing Forestry University（南京林业大学）
Forest land plays a vital role in global climate, ecosystems, farming and human living environments. Therefore, forest biomass estimation methods are necessary to monitor changes in the forest structure and function, which are key data in natural resources research. Although accurate forest biomass measurements are important in forest inventory and assessments, high-density measurements that involve airborne light detection and ranging (LiDAR) at a low flight height in large mountainous areas are highly expensive. The objective of this study was to quantify the aboveground biomass (AGB) of a p…
University of Cambridge（剑桥大学）；University of Cambridge Conservation Research Institute（剑桥大学保护研究所）；Royal Society for Protection of Birds（英国皇家鸟类保护协会）；University of Lincoln（林肯大学）
Developing a robust algorithm for automatic individual tree crown (ITC) detection from laser scanning datasets is important for tracking the responses of trees to anthropogenic change. Such approaches allow the size, growth and mortality of individual trees to be measured, enabling forest carbon stocks and dynamics to be tracked and understood. Many algorithms exist for structurally simple forests including coniferous forests and plantations. Finding a robust solution for structurally complex, species-rich tropical forests remains a challenge; existing segmentation algorithms often perform les…
University of Padova（帕多瓦大学）；Fondazione Edmund Mach（埃德蒙·马赫基金会）；Provincia Autonoma di Trento（特伦托自治省）
This work intends to lay the foundations for identifying the prevailing forest types and the delineation of forest units within private forest inventories in the Autonomous Province of Trento (PAT), using currently available remote sensing solutions. In particular, data from LiDAR and hyperspectral surveys of 2014 made available by PAT were acquired and processed. Such studies are very important in the context of forest management scenarios. The method includes defining tree species ground-truth by outlining single tree crowns with polygons and labeling them. Successively two supervised machin…
Estimating the Diameter at Breast Height of Trees in a Forest from RGB
General Robotics, Automation, Sensing and Perception (GRASP) Laboratory, University of Pennsylvania（通用机器人、自动化、传感与感知实验室，宾夕法尼亚大学）；Department of Forest Resources and Environmental Conservation, Virginia Tech（森林资源与环境保护系，弗吉尼亚理工学院）
Comparing remote sensing-based forest biomass mapping approaches using new forest inventory plots in contrasting forests in northeastern and southwestern China
School of GeoSciences, University of Edinburgh（爱丁堡大学地球科学学院）；Space Intelligence Ltd（空间智能有限公司）；Finnish Geospatial Research Institute（芬兰地理空间研究所）；Institute for Logic, Language and Computation, University of Amsterdam（阿姆斯特丹大学逻辑、语言与计算研究所）；Department of Forest Science, University of Helsinki（赫尔辛基大学森林科学系）；Jihua Lab（季华实验室）
UiT The Arctic University of Norway（挪威北极大学）；KSAT Kongsberg Satellite Services（康斯伯格卫星服务公司）；NORCE Norwegian Research Centre（挪威NORCE研究中心）；Norwegian University of Life Sciences（挪威生命科学大学）；Sokoine University of Agriculture（索科因农业大学）
This study derives regression models for above-ground biomass (AGB) estimation in miombo woodlands of Tanzania that utilise the high availability and low cost of Sentinel-1 data. The limited forest canopy penetration of C-band SAR sensors along with the sparseness of available ground truth restrict their usefulness in traditional AGB regression models. Therefore, we propose to use AGB predictions based on airborne laser scanning (ALS) data as a surrogate response variable for SAR data. This dramatically increases the available training data and opens for flexible regression models that capture…
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）
Generating accurate digital tree models from scanned environments is invaluable for forestry, agriculture, and other outdoor industries in tasks such as identifying biomass, fall hazards and traversability, as well as digital applications such as animation and gaming. Existing methods for tree reconstruction rely on feature identification (trunk, crown, etc) to heuristically segment a forest into individual trees and generate a branch structure graph, limiting their application to sparse trees and uniform forests. However, the natural world is a messy place in which trees present with signific…
Biomazon: A Multimodal Dataset for 3D Forest Structure and Biomass Modeling in the Amazon Basin
Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich（julich超级计算中心（JSC），julich研究所）；School of Engineering and Natural Sciences (SENS), University of Iceland（工程与自然科学学院（SENS），冰岛大学）；Global Land Monitoring Group, GFZ Helmholtz Centre for Geosciences（全球土地监测组，geofz赫尔姆霍兹研究中心）
Estimating Pasture Biomass from Top-View Images: A Dataset for Precision Agriculture
Data61, CSIRO（Data61，澳大利亚联邦科学工业研究组织）；Agriculture and Food, CSIRO（农业与食品，澳大利亚联邦科学工业研究组织）；Google（谷歌）；University of New England（新英格兰大学）；Meat & Livestock Australia（澳大利亚肉类与畜牧业局）
State-of-the-art techniques proposed for 6D object pose recovery depend on occlusion-free point clouds to accurately register objects in 3D space. To reduce this dependency, we introduce a novel architecture called Iterative Hough Forest with Histogram of Control Points that is capable of estimating occluded and cluttered objects' 6D pose given a candidate 2D bounding box. Our Iterative Hough Forest is learnt using patches extracted only from the positive samples. These patches are represented with Histogram of Control Points (HoCP), a "scale-variant" implicit volumetric description, which we …
HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation
Remote Sensing Technology Institute, German Aerospace Center (DLR)（远程传感技术研究所，德国航天中心）；Global Land Monitoring Group, Helmholtz German Research Centre for Geosciences (GFZ)（全球土地监测组，海德堡德国地理科学研究中心）；Department of Aerospace Engineering, University of the Bundeswehr Munich（航空航天工程系，联邦国防军 Munich 大学）；Chair of Data Science in Earth Observation (SiPEO), Technical University of Munich (TUM)（地球观测数据科学教授职位（SiPEO），慕尼黑技术大学）
3D-SAR Tomography and Machine Learning for High-Resolution Tree Height Estimation
University of Cambridge（剑桥大学）；United Nations Office for Outer Space Affairs（联合国外层空间事务厅）；UK Department for Energy Security and Net Zero（英国能源安全与净零部）；INSEC TEC；Drexel University（卓克索大学）
In this paper, we investigate estimating emergence and biomass traits from color images and elevation maps of wheat field plots. We employ a state-of-the-art deconvolutional network for segmentation and convolutional architectures, with residual and Inception-like layers, to estimate traits via high dimensional nonlinear regression. Evaluation was performed on two different species of wheat, grown in field plots for an experimental plant breeding study. Our framework achieves satisfactory performance with mean and standard deviation of absolute difference of 1.05 and 1.40 counts for emergence …
ReforesTree: A Dataset for Estimating Tropical Forest Carbon Stock with Deep Learning and Aerial Imagery
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）；University of Warwick（华威大学）；New York University（纽约大学）；WWF Switzerland（世界自然基金会瑞士分会）
University College Dublin（都柏林大学学院）；Dublin City University（都柏林城市大学）；Insight Centre for Data Analytics（洞察数据分析中心）
The dairy industry uses clover and grass as fodder for cows. Accurate estimation of grass and clover biomass yield enables smart decisions in optimizing fertilization and seeding density, resulting in increased productivity and positive environmental impact. Grass and clover are usually planted together, since clover is a nitrogen-fixing plant that brings nutrients to the soil. Adjusting the right percentages of clover and grass in a field reduces the need for external fertilization. Existing approaches for estimating the grass-clover composition of a field are expensive and time consuming - r…
New allometric models for the USA create a step-change in forest carbon estimation, modeling, and mapping
State University of New York College of Environmental Science and Forestry（纽约州立大学环境科学与林业学院）；USDA Forest Service（美国农业部林业局）；Northern Research Station（北方研究站）
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…
IBM Research（IBM研究院）；German Aerospace Center (DLR)（德国航空航天中心（DLR））
Vegetation, trees in particular, sequester carbon by absorbing carbon dioxide from the atmosphere. However, the lack of efficient quantification methods of carbon stored in trees renders it difficult to track the process. We present an approach to estimate the carbon storage in trees based on fusing multi-spectral aerial imagery and LiDAR data to identify tree coverage, geometric shape, and tree species -- key attributes to carbon storage quantification. We demonstrate that tree species information and their three-dimensional geometric shapes can be estimated from aerial imagery in order to de…
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）
StruMPL: Multi-task Dense Regression under Disjoint Partial Supervision and MNAR Labels
School of Geosciences, University of Edinburgh, UK（爱丁堡大学地球科学学院，英国）；National Centre for Earth Observation (NCEO), UK（英国地球观测国家中心）；Department of Spatial Sciences, Faculty of Environmental Sciences Czech University of Life Sciences Prague, Praha, Czech Republic（环境科学学院空间科学系，捷克布拉格生命科学大学）
A Deep Learning Approach to Estimate Canopy Height and Uncertainty by Integrating Seasonal Optical, SAR and Limited GEDI LiDAR Data over Northern Forests
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…
Forest Parameter Prediction by Multiobjective Deep Learning of Regression Models Trained with Pseudo-Target Imputation
UiT The Arctic University of Norway（挪威北极大学）；Kongsberg Satellite Services（康斯伯格卫星服务公司）；NORCE Norwegian Research Institute（NORCE挪威研究所）；Norwegian University of Life Sciences（挪威生命科学大学）
Tomographic SAR Reconstruction for Forest Height Estimation
University of Cambridge（剑桥大学）；United Nations Office for Outer Space Affairs（联合国外层空间事务厅）；UK Department for Energy Security and Net Zero（英国能源安全与净零部）；INSEC TEC；Drexel University（卓克索大学）
本文提出直接由二维 SLC SAR 图像利用深度学习估计森林冠层高度，并评估 SLC 数量影响；结果表明绕过完整层析处理会使误差增加 16%–21%。
Transformers vs. Recurrent Models for Estimating Forest Gross Primary Production
IEF, Leipzig University（莱比锡大学IEF）；iDiv；MPI PKS（马克斯·普朗克研究所）；IPL, Universitat de València（瓦伦西亚大学IPL）；Bioclimatology, University of Göttingen（哥廷根大学生物气候学系）；GEMA, Universidad de Medellín（梅迪纳大学GEMA）
A Novel Large Vision Foundation Model (LVFM)-based Approach for Generating High-Resolution Canopy Height Maps in Plantations for Precision Forestry Management
A method for estimating forest carbon storage distribution density via artificial intelligence generated content model
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）
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）
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））
Label-Efficient 3D Forest Mapping: Self-Supervised and Transfer Learning for Instance Segmentation, Semantic Segmentation, and Species Classification
Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Helmholtz Institute Freiberg for Resource Technology (HIF)（德累斯顿-罗斯托克亥姆霍兹中心（HZDR）、弗里贝格资源技术亥姆霍兹研究所（HIF））；Remote Sensing and Geoinformatics, Freie Universität Berlin（柏林自由大学遥感与地理信息学系）；Institute of Geomatics, BOKU University（博科尼大学测绘学院）；Faculty of Electrical and Computer Engineering, University of Iceland（爱沙尼亚大学电气与计算机工程学院）
