This is the Authors' accepted version only! The final version of this paper can be located at Springer.com as part of the Current Forestry Reports (2017) 3: 32. doi:10.1007/s40725-017-0047-2
Purpose of review: This paper presents a review of the current state of the art in remote sensing based monitoring of forest disturbances and forest degradation from optical Earth Observation data. Part one comprises an overview of currently available optical remote sensing sensors, which can be used for forest disturbance and degradation mapping. Part two reviews the two main categories of existing approaches: classical image-to-image change detection and time series analysis. Recent findings: With the launch of the Sentinel-2a satellite and available Landsat imagery, time series analysis has become the most promising but also most demanding category of degradation mapping approaches. Four time series classification methods are distinguished. The methods are explained and their benefits and drawbacks are discussed. A separate chapter presents a number of recent forest degradation mapping studies for two different ecosystems: temperate forests with a geographical focus on Europe and tropical forests with a geographical focus on Africa. Summary: The review revealed that a wide variety of methods for the detection of forest degradation is already available. Today, the main challenge is to transfer these approaches to high resolution time series data from multiple sensors. Future research should also focus on the classification of disturbance types and the development of robust up-scalable methods to enable near real time disturbance mapping in support of operational reactive measures.
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
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）
UiT The Arctic University of Norway（挪威北极大学）；NORCE Norwegian Research Centre（挪威NORCE研究中心）；Norwegian Institute for Nature Research（挪威自然研究所）
Several generic methods have recently been developed for change detection in heterogeneous remote sensing data, such as images from synthetic aperture radar (SAR) and multispectral radiometers. However, these are not well suited to detect weak signatures of certain disturbances of ecological systems. To resolve this problem we propose a new approach based on image-to-image translation and one-class classification (OCC). We aim to map forest mortality caused by an outbreak of geometrid moths in a sparsely forested forest-tundra ecotone using multisource satellite images. The images preceding an…
A Satellite Band Selection Framework for Amazon Forest Deforestation Detection Task
Institute of Science and Technology Universidade Federal de São Paulo（圣保罗联邦大学科学与技术学院）；Escola de Artes, Ciências e Humanidades University of Sao Paulo（圣保罗大学艺术、科学与人文学院）
Country-wide, high-resolution monitoring of forest browning with Sentinel-2
ETH Zurich（苏黎世联邦理工学院）；ETH AI Center（ETH人工智能中心）；Swiss Data Science Center（瑞士数据科学中心）；EPFL（洛桑联邦理工学院）；Swiss Federal Institute for Forest, Snow and Landscape Research WSL（瑞士联邦森林、雪与景观研究所）；University of Bern（伯尔尼大学）；Oeschger Centre for Climate Change Research（厄施格气候变化研究中心）
Guidelines for External Disturbance Factors in the Use of OCR in Real-World Environments
National Institute of Advanced Industrial Science and Technology (AIST)（国家先进工业科学与技术研究院）；NEC Corporation（日本电气株式会社）；Ricoh Co., Ltd.（理光株式会社）；Fujitsu Limited（富士通株式会社）；Toshiba Digital Solutions Corporation（东芝数字解决方案公司）
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）
deadtrees.earth-aerial: A Multi-Resolution Aerial Image Dataset for Tree Cover and Mortality Detection
Chair of Sensor-based Geoinformatics, University of Freiburg（传感器基于地理信息学系，弗赖堡大学）；EcoVision Lab, DM3L, University of Zurich（生态视觉实验室，苏黎世大学）；Institute for Earth System Science and Remote Sensing, Leipzig University（地球系统科学与遥感研究所，莱比锡大学）；Washington University, St. Louis（斯蒂芬斯敦大学）
University of California, Los Angeles（加州大学洛杉矶分校）；California Institute of Technology（加州理工学院）；NASA-Jet Propulsion Laboratory（美国国家航空航天局喷气推进实验室）；Virginia Polytechnic Institute and State University（弗吉尼亚理工学院暨州立大学）
Monitoring changes in tree cover for rapid assessment of deforestation is considered the critical component of any climate mitigation policy for reducing carbon. Here, we map tropical tree cover and deforestation between 2015 and 2022 using 5 m spatial resolution Planet NICFI satellite images over the state of Mato Grosso (MT) in Brazil and a U-net deep learning model. The tree cover for the state was 556510.8 km$^2$ in 2015 (58.1 % of the MT State) and was reduced to 141598.5 km$^2$ (14.8 % of total area) at the end of 2021. After reaching a minimum deforested area in December 2016 with 6632.…
Low-Cost Tree Crown Dieback Estimation Using Deep Learning-Based Segmentation
University of Cambridge（剑桥大学）；Universidad de Alcalá（阿尔卡拉大学）；Institute of Forest Sciences (INIA-CSIC)（森林科学研究所（INIA-CSIC））；Queen Mary University of London（伦敦玛丽女王大学）；Digital Environment Research Institute（数字环境研究所）
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）
