Monitoring tree-crown scale autumn leaf phenology in a temperate forest with an integration of PlanetScope and drone remote sensing observations

Wu, Shengbiao; Wang, Jing; Yan, Zhengbing; Song, Guangqin; Chen, Yang; Ma, Qin; Deng, Meifeng; Wu, Yuntao; Zhao, Yanxin; Guo, Zhaoxin; Yuan, Zuoqiang; Dai, Guang; Xu, Xiangtao; Yang, Xi; Su, Yanjun; Liu, Lingli; Wu, Jin

doi:10.1016/j.isprsjprs.2020.10.017

Cited by 64 publications

(23 citation statements)

References 81 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This is termed the typical-length scale 40 . For instance, an individual tree, required for mapping the EBV classes species populations 18 and species traits, cannot be identified at the typical-length-scale spatial resolution of a Sentinel-2 or Landsat-7 and 8 image pixel, but may be feasible using a very-high-resolution image (for example, PlanetScope) 41 , which could then be combined, or fused, with satellite hyperspectral imagery such as the (operational) Italian PRISMA and German DESIS and (planned) National Aeronautics and Space Administration (NASA) and European Space Agency satellites (SBG and CHIME). Fusing of data from these different (and emerging) sensor technologies allows remote sensing biodiversity products to be transformed into EBVs, and ultimately higher-level indicators, although an important caveat is that efficacious image fusion requires skilled image processing approaches 42 .…”

Section: Remote Sensing Status: Maturitymentioning

confidence: 99%

Priority list of biodiversity metrics to observe from space

et al. 2021

View full text Add to dashboard Cite

Biodiversity encompasses the complex variety of life at all scales, ranging from genes to species to ecosystems. It encapsulates the structure, function, distribution, traits and composition of all living things. Crisis-level losses of biodiversity are stimulating action from local to global scales, as evidenced by establishment of the United Nations Sustainable Development Goals (SDGs) and Aichi targets and the current post-2020 negotiation of the Convention on Biological Diversity (CBD), as well as the first round of risk assessments by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) 1 . In response to these losses of biodiversity, the Group on Earth Observations Biodiversity Observation Network (GEO BON) 2,3 proposes a common framework of essential biodiversity variables (EBVs) 4 for monitoring biodiversity. These EBVs form a core set of complementary biological measurements for capturing considerable biodiversity change and are produced by

show abstract

Section: Remote Sensing Status: Maturitymentioning

confidence: 99%

Priority list of biodiversity metrics to observe from space

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Combining UAVs and deep learning models should provide instantaneous information about crop status, soil type, and disease/pest attack, which were impossible during the past millennia. Precise and automatic crop classification using UAV-based remote sensing imagery and deep learning techniques represent a fundamental task for many smart farming applications, including crop yield estimation [ 5 , 119 ], crop surveying/monitoring [ 107 , 113 ], water stress monitoring [ 28 ], precise pesticide and liquid fertilizers spraying [ 35 ]. These tasks could lead to crop production increasement, cost reduction, and save a lot of precious time providing precious information that should help the farmers to make instant decisions.…”

Section: Introductionmentioning

confidence: 99%

Deep learning techniques to classify agricultural crops through UAV imagery: a review

Bouguettaya

Zarzour

Kechida

et al. 2022

Neural Comput & Applic

137

View full text Add to dashboard Cite

During the last few years, Unmanned Aerial Vehicles (UAVs) technologies are widely used to improve agriculture productivity while reducing drudgery, inspection time, and crop management cost. Moreover, they are able to cover large areas in a matter of a few minutes. Due to the impressive technological advancement, UAV-based remote sensing technologies are increasingly used to collect valuable data that could be used to achieve many precision agriculture applications, including crop/plant classification. In order to process these data accurately, we need powerful tools and algorithms such as Deep Learning approaches. Recently, Convolutional Neural Network (CNN) has emerged as a powerful tool for image processing tasks achieving remarkable results making it the state-of-the-art technique for vision applications. In the present study, we reviewed the recent CNN-based methods applied to the UAV-based remote sensing image analysis for crop/plant classification to help researchers and farmers to decide what algorithms they should use accordingly to their studied crops and the used hardware. Fusing different UAV-based data and deep learning approaches have emerged as a powerful tool to classify different crop types accurately. The readers of the present review could acquire the most challenging issues facing researchers to classify different crop types from UAV imagery and their potential solutions to improve the performance of deep learning-based algorithms.

show abstract

“…Recently, PlanetScope data were used to derive phenology (Cheng et al, 2020; Francini et al, 2020). Bradshaw et al (2019) related RSIs to UAV and PlanetScope data in southern African semi‐arid areas, while Wu et al (2021) monitored tree‐crown scale leaf phenology using PlanetScope and UAV data based on RSIs. Collin et al (2018) used superspectral WorldView 3 data to map vegetation height and composition using RSI.…”

Section: The Future Of Rsis To Assess Impacts Of Frsm On Riparian Treesmentioning

confidence: 99%

The use of remote sensing indices to understand flood‐recharged soil moisture impacts on trees in semi‐arid floodplains: A review

et al. 2022

View full text Add to dashboard Cite

Remote sensing indices (RSIs) are essential for detection, mapping and monitoring of floods, soil moisture and tree characteristics. An understanding of the nexus between flood-recharged soil moisture (FRSM) and endangered biodiversity tree hotspots in semi-arid floodplains is critical. Specifically, the influence of FRSM on biophysical characteristics of keystone tree species with social, economic, ecological and environmental functions is crucial. However, the use of RSIs to understand the link between episodic floods, FRSM and trees, as well as their respective spatiotemporal dynamics within the semi-arid floodplains, remains largely unexplored. Hence, this study reviewed literature on the adoption of RSIs in understanding short-term phenological and long-term tree structural changes arising from flood-related infiltration. The reviewed literature shows the predominance of the normalised difference water index, the land surface water index and normalised difference vegetation index for flood, FRSM and tree mapping. The review also shows the emergence of SAR-based normalised difference flood index for understanding the impacts of FRSM on riparian trees. Although the review identified an increase in data availability, however, there are high costs of active commercial remote sensing data of superior quality and computing resources. Furthermore, available free data are incapable of penetrating canopies and have coarse spatial resolution constraining the use of RSIs to understand the impacts of FRSM on trees. In view of increasing free SAR data and cloudbased computing resources, application of emerging deep learning algorithms offers prospects for the use of RSIs to understand the impacts of FRSM on riparian trees.

show abstract

Monitoring tree-crown scale autumn leaf phenology in a temperate forest with an integration of PlanetScope and drone remote sensing observations

Cited by 64 publications

References 81 publications

Priority list of biodiversity metrics to observe from space

Priority list of biodiversity metrics to observe from space

Deep learning techniques to classify agricultural crops through UAV imagery: a review

The use of remote sensing indices to understand flood‐recharged soil moisture impacts on trees in semi‐arid floodplains: A review

Contact Info

Product

Resources

About