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）
Forests function as crucial carbon reservoirs on land, and their carbon sinks can efficiently reduce atmospheric CO2 concentrations and mitigate climate change. Currently, the overall trend for monitoring and assessing forest carbon stocks is to integrate ground monitoring sample data with satellite remote sensing imagery. This style of analysis facilitates large-scale observation. However, these techniques require improvement in accuracy. We used GF-1 WFV and Landsat TM images to analyze Huize County, Qujing City, Yunnan Province in China. Using the style transfer method, we introduced Swin Transformer to extract global features through attention mechanisms, converting the carbon stock estimation into an image translation.
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…
This paper proposes a system for automatic surface volume monitoring from time series of SkySat pushframe imagery. A specific challenge of building and comparing large 3D models from SkySat data is to correct inconsistencies between the camera models associated to the multiple views that are necessary to cover the area at a given time, where these camera models are represented as Rational Polynomial Cameras (RPCs). We address the problem by proposing a date-wise RPC refinement, able to handle dynamic areas covered by sets of partially overlapping views. The cameras are refined by means of a ro…
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…
Nagoya University（名古屋大学）；Chubu University（中部大学）；Tokyo Institute of Technology（东京工业大学）；Advanced Industrial Science and Technology（产业技术综合研究所）
In this paper, we propose a method for cloud removal from visible light RGB satellite images by extending the conditional Generative Adversarial Networks (cGANs) from RGB images to multispectral images. Satellite images have been widely utilized for various purposes, such as natural environment monitoring (pollution, forest or rivers), transportation improvement and prompt emergency response to disasters. However, the obscurity caused by clouds makes it unstable to monitor the situation on the ground with the visible light camera. Images captured by a longer wavelength are introduced to reduce…
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）
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 …
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…
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…
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…
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 me…
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）
Enhancing Tree Type Detection in Forest Fire Risk Assessment: Multi-Stage Approach and Color Encoding with Forest Fire Risk Evaluation Framework for UAV Imagery
There is much current interest in using multi-sensor airborne remote sensing to monitor the structure and biodiversity of forests. This paper addresses the application of non-parametric image registration techniques to precisely align images obtained from multimodal imaging, which is critical for the successful identification of individual trees using object recognition approaches. Non-parametric image registration, in particular the technique of optimizing one objective function containing data fidelity and regularization terms, provides flexible algorithms for image registration. Using a sur…
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…
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）
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）
High Resolution Flood Extent Detection Using Deep Learning with Random Forest Derived Training Labels
Department of Civil Engineering Florida State University Tallahassee, FL, USA；Dept. of Biosystems \& Agricultural Engineering Michigan State University East Lansing, MI, USA
ForCM: Forest Cover Mapping from Multispectral Sentinel-2 Image by Integrating Deep Learning with Object-Based Image Analysis
BRAC University（布拉克大学）；Charles Sturt University（查尔斯·斯特劳特大学）；University of New England（新英格兰大学）；University of Technology Sydney（技术悉尼大学）；Griffith University（格里菲斯大学）；Universiti Kebangsaan Malaysia (UKM)（马来亚大学）；Federation University Australia（联邦大学澳大利亚）；RMIT University（皇家墨尔本理工大学）
The Hong Kong Polytechnic University（香港理工大学）；Bavarian Forest National Park（巴伐利亚森林国家公园）；Albert-Ludwigs-Universität Freiburg（弗赖堡大学）
The stability and ability of an ecosystem to withstand climate change is directly linked to its biodiversity. Dead trees are a key indicator of overall forest health, housing one-third of forest ecosystem biodiversity, and constitute 8%of the global carbon stocks. They are decomposed by several natural factors, e.g. climate, insects and fungi. Accurate detection and modeling of dead wood mass is paramount to understanding forest ecology, the carbon cycle and decomposers. We present a novel method to construct precise shape contours of dead trees from aerial photographs by combining established…
Parameter-Efficient Fine-Tuning of Vision Foundation Model for Forest Floor Segmentation from UAV Imagery
Bonn-Rhein-Sieg University of Applied Sciences（博恩-莱茵-锡格应用科学大学）；Department of Computer Science（计算机科学系）；Institute for Artificial Intelligence and Autonomous Systems (A2S)（人工智能与自主系统研究所）；Institute of Technology, Resource and Energy-efficient Engineering (TREE)（技术与资源节能工程研究所）
本文针对无人机林地地表分割难题，采用 PEFT 微调 SAM 并改造其掩码解码器实现无提示自动分割，证明适配器方法 mIoU 最高，LoRA 更轻量。
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）
University of Copenhagen（哥本哈根大学）；University of Münster（明斯特大学）
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. …
Real-Time Localization and Bimodal Point Pattern Analysis of Palms Using UAV Imagery
City University of Hong Kong（香港城市大学）；Hong Kong Centre for Cerebro-Cardiovascular Health Engineering（香港脑心血管健康工程中心）；Xidian University（西安电子科技大学）；Wake Forest University（维克森林大学）；University of California, Berkeley（加州大学伯克利分校）；Tulane University（杜兰大学）；Lingnan University（岭南大学）
Global climate change has had a drastic impact on our environment. Previous study showed that pest disaster occured from global climate change may cause a tremendous number of trees died and they inevitably became a factor of forest fire. An important portent of the forest fire is the condition of forests. Aerial image-based forest analysis can give an early detection of dead trees and living trees. In this paper, we applied a synthetic method to enlarge imagery dataset and present a new framework for automated dead tree detection from aerial images using a re-trained Mask RCNN (Mask Region-ba…
The Amazon rainforests have been suffering widespread damage, both via natural and artificial means. Every minute, it is estimated that the world loses forest cover the size of 48 football fields. Deforestation in the Amazon rainforest has led to drastically reduced biodiversity, loss of habitat, climate change, and other biological losses. In this respect, it has become essential to track how the nature of these forests change over time. Image classification using deep learning can help speed up this process by removing the manual task of classifying each image. Here, it is shown how convolut…
University of Cambridge（剑桥大学）；Fondazione E. Mach（埃马努埃莱·马赫基金会）；University of Oxford（牛津大学）；The University of Queensland（昆士兰大学）；Natural England（英国自然署）
Recognising individual trees within remotely sensed imagery has important applications in forest ecology and management. Several algorithms for tree delineation have been suggested, mostly based on locating local maxima or inverted basins in raster canopy height models (CHMs) derived from Light Detection And Ranging (LiDAR) data or photographs. However, these algorithms often lead to inaccurate estimates of forest stand characteristics due to the limited information content of raster CHMs. Here we develop a 3D tree delineation method which uses graph cut to delineate trees from the full 3D LiD…
Cross-Domain Dead Tree Detection via Knowledge Distillation in Aerial Imagery
CSC – IT Center for Science Ltd.（CSC信息科技研究中心有限公司）；Department of Forest Sciences, University of Helsinki（赫尔辛基大学森林科学系）；KOKO Forest Ltd.（KOKO森林有限公司）；School of Forest Sciences, University of Eastern Finland（东芬兰大学森林科学学院）
University of Shanghai for Science and Technology（上海理工大学）；Indiana University Bloomington（印第安纳大学伯明顿分校）；Tianjin University（天津大学）；Suffolk University（萨福克大学）
Vegetation is the natural linkage connecting soil, atmosphere and water. It can represent the change of land cover to a certain extent and serve as an indicator for global change research. Methods for measuring coverage can be divided into two types: surface measurement and remote sensing. Because vegetation cover has significant spatial and temporal differentiation characteristics, remote sensing has become an important technical means to estimate vegetation coverage. This paper firstly uses U-net to perform remote sensing image semantic segmentation training, then uses the result of semantic…
Northern Arizona University（北亚利桑那大学）；Air Force Research Laboratory（美国空军研究实验室）
Wildfires are one of the costliest and deadliest natural disasters in the US, causing damage to millions of hectares of forest resources and threatening the lives of people and animals. Of particular importance are risks to firefighters and operational forces, which highlights the need for leveraging technology to minimize danger to people and property. FLAME (Fire Luminosity Airborne-based Machine learning Evaluation) offers a dataset of aerial images of fires along with methods for fire detection and segmentation which can help firefighters and researchers to develop optimal fire management …
In this work, we present a methodology for monitoring man-made, construction-like activities in low-resolution SAR imagery. Our source of data is the European Space Agency Sentinel-l satellite which provides global coverage at a 12-day revisit rate. Despite limitations in resolution, our methodology enables us to monitor activity levels (i.e. presence of vehicles, equipment) of a pre-defined location by analyzing the texture of detected SAR imagery. Using an exploratory dataset, we trained a support vector machine (SVM), a random binary forest, and a fully-connected neural network for classifi…
University of Cambridge（剑桥大学）；Royal Society for Protection of Birds（英国皇家鸟类保护协会）；Jambi University（占碑大学）；PT Restorasi Ekosistem Indonesia（印度尼西亚生态系统恢复公司）
Logged forests cover four million square kilometres of the tropics and restoring these forests is essential if we are to avoid the worst impacts of climate change, yet monitoring recovery is challenging. Tracking the abundance of visually identifiable, early-successional species enables successional status and thereby restoration progress to be evaluated. Here we present a new pipeline, SLIC-UAV, for processing Unmanned Aerial Vehicle (UAV) imagery to map early-successional species in tropical forests. The pipeline is novel because it comprises: (a) a time-efficient approach for labelling crow…
Automated Linear Disturbance Mapping via Semantic Segmentation of Sentinel-2 Imagery
University of Winnipeg（温尼伯大学）；Hatfield Consultants Group（哈特菲尔德咨询集团）；University of Manitoba（曼尼托巴大学）；Pachama（帕查玛公司）；Environment and Climate Change Canada（加拿大环境与气候变化部）
The estimation of deforestation in the Amazon Forest is challenge task because of the vast size of the area and the difficulty of direct human access. However, it is a crucial problem in that deforestation results in serious environmental problems such as global climate change, reduced biodiversity, etc. In order to effectively solve the problems, satellite imagery would be a good alternative to estimate the deforestation of the Amazon. With a combination of optical images and Synthetic aperture radar (SAR) images, observation of such a massive area regardless of weather conditions become poss…
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））
Urban trees help regulate temperature, reduce energy consumption, improve urban air quality, reduce wind speeds, and mitigating the urban heat island effect. Urban trees also play a key role in climate change mitigation and global warming by capturing and storing atmospheric carbon-dioxide which is the largest contributor to greenhouse gases. Automated tree detection and species classification using aerial imagery can be a powerful tool for sustainable forest and urban tree management. Hence, This study first offers a pipeline for generating labelled dataset of urban trees using Google Map's a…
