IEEE Transactions on Geoscience and Remote Sensing, Journal Year: 2025, Volume and Issue: 63, P. 1 - 11
Published: Jan. 1, 2025
Language: Английский
Remote Sensing, Journal Year: 2022, Volume and Issue: 14(13), P. 3205 - 3205
Published: July 4, 2022
In recent years, technological advances have led to the increasing use of unmanned aerial vehicles (UAVs) for forestry applications. One emerging field drone application is forest health monitoring (FHM). Common approaches FHM involve small-scale resource-extensive fieldwork combined with traditional remote sensing platforms. However, highly dynamic nature forests requires timely and repetitive data acquisition, often at very high spatial resolution, where conventional techniques reach limits feasibility. UAVs shown that they can meet demands flexible operation resolution. This also reflected in a rapidly growing number publications using drones study health. Only few reviews exist which do not cover whole research history UAV-based FHM. Since comprehensive review becoming critical identify gaps, trends, drawbacks, we offer systematic analysis 99 papers covering last ten years related threatened by biotic abiotic stressors. Advances technology are being adopted put into practice, further improving economical UAVs. Despite many advantages UAVs, such as their flexibility, relatively low costs, possibility fly below cloud cover, identified some shortcomings: (1) multitemporal long-term clearly underrepresented; (2) rare hyperspectral LiDAR sensors must drastically increase; (3) complementary from other RS sources sufficiently exploited; (4) lack standardized workflows poses problem ensure uniformity; (5) complex machine learning algorithms obscure interpretability hinders widespread adoption; (6) pipeline acquisition final relies on commercial software expense open-source tools.
Language: Английский
Citations
151
Remote Sensing, Journal Year: 2021, Volume and Issue: 13(13), P. 2596 - 2596
Published: July 2, 2021
Replanting trees helps with avoiding desertification, reducing the chances of soil erosion and flooding, minimizing risks zoonotic disease outbreaks, providing ecosystem services livelihood to indigenous people, in addition sequestering carbon dioxide for mitigating climate change. Consequently, it is important explore new methods technologies that are aiming upscale fast-track afforestation reforestation (A/R) endeavors, given many current tree planting strategies not cost effective over large landscapes, suffer from constraints associated time, energy, manpower, nursery-based seedling production. UAV (unmanned aerial vehicle)-supported seed sowing (UAVsSS) can promote rapid A/R a safe, cost-effective, fast environmentally friendly manner, if performed correctly, even otherwise unsafe and/or inaccessible terrains, supplementing overall manual efforts globally. In this study, we reviewed recent literature on UAVsSS, analyze status technology. Primary UAVsSS applications were found be areas post-wildfire reforestation, mangrove restoration, forest restoration after degradation, weed eradication, desert greening. Nonetheless, low survival rates seeds, future diversity, weather limitations, financial constraints, seed-firing accuracy concerns determined as major challenges operationalization. Based our survey qualitative analysis, twelve recommendations—ranging need publishing germination results linking operations offset markets—are provided advancement applications.
Language: Английский
Citations
117
Forests, Journal Year: 2022, Volume and Issue: 13(6), P. 911 - 911
Published: June 10, 2022
Unmanned aerial vehicles (UAVs) are platforms that have been increasingly used over the last decade to collect data for forest insect pest and disease (FIPD) monitoring. These machines provide flexibility, cost efficiency, a high temporal spatial resolution of remotely sensed data. The purpose this review is summarize recent contributions identify knowledge gaps in UAV remote sensing FIPD A systematic was performed using preferred reporting items reviews meta-analysis (PRISMA) protocol. We reviewed full text 49 studies published between 2015 2021. parameters examined were taxonomic characteristics, type sensor, collection pre-processing, processing analytical methods, software used. found number papers on topic has increased years, with most being located China Europe. main FIPDs studied pine wilt (PWD) bark beetles (BB) multirotor architectures. Among sensor types, multispectral red–green–blue (RGB) bands monitoring tasks. Regarding random (RF) deep learning (DL) classifiers frequently applied imagery processing. This paper discusses advantages limitations associated use UAVs methods FIPDs, research challenges presented.
Language: Английский
Citations
77
Frontiers in Plant Science, Journal Year: 2023, Volume and Issue: 14
Published: March 22, 2023
Crop protection is a key activity for the sustainability and feasibility of agriculture in current context climate change, which causing destabilization agricultural practices an increase incidence or invasive pests, growing world population that requires guaranteeing food supply chain ensuring security. In view these events, this article provides contextual review six sections on role artificial intelligence (AI), machine learning (ML) other emerging technologies to solve future challenges crop protection. Over time, has progressed from primitive 1.0 (Ag1.0) through various technological developments reach level maturity closelyin line with Ag5.0 (section 1), characterized by successfully leveraging ML capacity modern devices machines perceive, analyze actuate following main stages precision 2). Section 3 presents taxonomy algorithms support development implementation protection, while section 4 analyses scientific impact basis extensive bibliometric study >120 algorithms, outlining most widely used deep (DL) techniques currently applied relevant case studies detection control diseases, weeds plagues. 5 describes 39 fields smart sensors advanced hardware devices, telecommunications, proximal remote sensing, AI-based robotics will foreseeably lead next generation perception-based, decision-making actuation systems digitized, real-time realistic Ag5.0. Finally, 6 highlights conclusions final remarks.
Language: Английский
Citations
70
Sustainability, Journal Year: 2023, Volume and Issue: 15(21), P. 15444 - 15444
Published: Oct. 30, 2023
Remote sensing (RS) techniques offer advantages over other methods for measuring soil properties, including large-scale coverage, a non-destructive nature, temporal monitoring, multispectral capabilities, and rapid data acquisition. This review highlights the different detection methods, types, parts, applications of RS in measurements, as well disadvantages measurements properties. The choice depends on specific requirements task because it is important to consider limitations each method, context objective determine most suitable technique. paper follows well-structured arrangement after investigating existing literature ensure well-organized, coherent covers all essential aspects related studying advancement using While several remote are available, this suggests spectral reflectance, which entails satellite tools based its global high spatial resolution, long-term monitoring non-invasiveness, cost effectiveness. Conclusively, has improved property various but more research needed calibration, sensor fusion, artificial intelligence, validation, machine learning enhance accuracy applicability.
Language: Английский
Citations
70
Forest Ecology and Management, Journal Year: 2024, Volume and Issue: 556, P. 121595 - 121595
Published: Feb. 16, 2024
Bark beetle disturbances increasingly threaten structure and functionality of temperate boreal forests globally. The early detection bark beetle-infested trees, i.e. before beetles' emergence from the breeding tree, is essential for an effective outbreak mitigation. Terrestrial control surveys as traditionally employed infestation detection, however, are resource-intensive approach their limits in difficult terrain during mass outbreaks. Developments remote sensing algorithms giving hope that early-infested trees will be detectable remotely, thereby improving success management efficacy. Yet, a comprehensive quantitative evaluation approaches currently being developed lacking to date. This review synthesises state-of-the-art recent research on (or green-attack) by sensing, places it context with underlying biological constraints, technical opportunities potential applications. Since each beetle-host tree system has specific characteristics detectability, we focus greatest impact European forests, spruce (Ips typographus), which attacks Norway (Picea abies). By screening published within period 2000–2022, included 26 studies our analyses. All reviewed were purely exploratory, testing variety data and/or classification relatively limited spatial temporal coverage. Among tested platforms sensor types, satellite multispectral imagery most frequently investigated. Promising spectral wavelength range or index highly varied among regions. Timeliness accuracy found insufficient efficient management, regardless platform, type, resolution applied. main reasons preventing better performance include rapid development I. typographus combination delayed variable vitality response crown, frequent cloud cover spruce-dominated regions across Europe. In conclusion, current survey methods cannot yet replace terrestrial timely management. Nevertheless, they might supportive either back-up regular surveys, situations, e.g. detect hibernation accessibility, extensively managed without sufficient capacity. We suggest term 'early detection' used consistently synonym 'pre-emergence avoid ambiguity. Finally, provide recommendations future based lessons learned analysed, namely use more rigorous targeted study design, ensure interdisciplinarity, communicate results explicitly.
Language: Английский
Citations
21
Computers and Electronics in Agriculture, Journal Year: 2022, Volume and Issue: 198, P. 107035 - 107035
Published: May 10, 2022
Language: Английский
Citations
70
Remote Sensing, Journal Year: 2023, Volume and Issue: 15(3), P. 778 - 778
Published: Jan. 29, 2023
The automatic detection of tree crowns and estimation crown areas from remotely sensed information offer a quick approach for grasping the dynamics forest ecosystems are great significance both biodiversity ecosystem conservation. Among various types remote sensing data, unmanned aerial vehicle (UAV)-acquired RGB imagery has been increasingly used area estimation; method efficient advantages relies heavily on deep learning models. However, not thoroughly investigated in deciduous forests with complex structures. In this study, we evaluated two widely used, deep-learning-based delineation approaches (DeepForest Detectree2) to assess their potential detecting UAV-acquired an alpine, temperate complicated species composition. A total 499 digitized crowns, including four dominant species, corresponding, accurate inventory data 1.5 ha study plot were treated as training validation datasets. We attempted identify effective model delineate explore effects spatial resolution performance, well extracted areas, detailed field inventory. results show that models, which Detectree2 (F1 score: 0.57) outperformed DeepForest 0.52), could be transferred predict successfully. had obvious effect accuracy detection, especially when was greater than 0.1 m. Furthermore, Dectree2 estimate accurately, highlighting its robustness delineation. addition, performance varied among different species. These indicate efficiently individual high-resolution optical images, while demonstrating applicability Detectree2, and, thus, have transferable strategies can applied other ecosystems.
Language: Английский
Citations
38
Remote Sensing, Journal Year: 2023, Volume and Issue: 15(9), P. 2263 - 2263
Published: April 25, 2023
When it comes to forest management and protection, knowledge is key. Therefore, mapping crucial obtain the required towards profitable resource exploitation increased resilience against wildfires. Within this context, paper presents a literature review on tree classification segmentation using data acquired by unmanned aerial vehicles, with special focus last decade (2013–2023). The latest research trends in field are presented analyzed two main vectors, namely: (1) data, where used sensors structures resumed; (2) methods, remote sensing analysis methods described, particular machine learning approaches. study methodology filtered 979 papers, which were then screened, resulting 144 works included paper. These systematically organized year, keywords, purpose, sensors, used, easily allowing readers have wide, but at same time detailed, view of automatic vehicles. This shows that image processing techniques applied forestry tasks focused improving accuracy interpretability results multi-modal 3D information, AI methods. Most use RGB or multispectral cameras, LiDAR scanners, individually. Classification mostly carried out supervised while uses unsupervised techniques.
Language: Английский
Citations
30
Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 219, P. 108785 - 108785
Published: March 6, 2024
Uncrewed Aerial Vehicles (UAVs) have emerged as a promising tool for complementing terrestrial surveys, offering unique advantages forest health monitoring (FHM). UAVs the potential to improve or even replace core tasks such crown condition assessment, bridging gap between ground-based surveys and traditional remote sensing platforms. However, present approaches not yet fully exploited very high temporal resolution flexible convenient utilization that offer under cloudy skies. In this paper, we provide standardized data pipeline semi-automatically generate reference by merging UAV-based related species-specific health. Furthermore, investigated of Convolutional Neural Networks (CNNs) classify main tree species their conditions based on data. Therefore, acquired multispectral drone imagery 235 different ICP large scale plots (Level-I plots) distributed across Bavaria three consecutive years (2020–2022). Using highly heterogeneous time-series dataset, encompassing diverse weather lighting conditions, stand characteristics, spatial distribution study areas, successfully classified five species, genus level classes dead trees, including status occurring in Germany. This way managed 14 distinct with an average macro F1-score 0.61 using EfficientNet CNN architecture. The highest class-specific apart from class trees (0.97) was achieved Picea abies healthy (0.80). If participating countries Forests program adopt our approach harmonize monitoring, many could be reduced replaced, leading significant time cost savings. We open-source analysis strategies can potentially extended throughout Europe. Our findings demonstrate UAV deep learning modernize management efficiency sustainability. recommend integrating drones ground systems take advantage benefits.
Language: Английский
Citations
15