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Retrieving Leaf Area Index Using Remote Sensing
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Retrieving Leaf Area Index Using Remote Sensing

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: 20 January 2025 | Viewed by 12173

Special Issue Editors

Dr. Yanghui Kang

E-Mail Website
Guest Editor
Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA 94720, USA
Interests: remote sensing of vegetation; terrestrial carbon cycle; agroecosystem modeling; machine learning
Dr. Feng Gao

E-Mail Website
Guest Editor
Hydrology and Remote Sensing Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD 20705, USA
Interests: multi-sensor data fusion; crop phenology; biophysical parameter retrieval; time series analysis; near-real-time mapping
Special Issues, Collections and Topics in MDPI journals
Dr. Rasmus Houborg

E-Mail Website1 Website2
Guest Editor
Planet Labs, 645 Harrison St, San Francisco, CA 94107, USA
Interests: multi-sensor data fusion; radiometric harmonization; machine learning; precision agriculture; satellite-based retrieval of vegetation biophysical properties and functional traits; satellite-based water use and productivity estimation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Leaves are the primary sites for energy, carbon, and water exchange between plants and the atmosphere. Leaf Area Index (LAI), defined as the amount of single-sided leaf area per unit of ground area, is an essential variable for modeling and understanding climate–ecosystem interactions. Remote sensing techniques are used to retrieve LAI at various spatial scales. For decades, remote-sensing-derived LAI data products have boosted scientific advancements in global vegetation change, agroecosystem monitoring, and earth system modeling. Important applications such as climate change mitigation, agricultural sustainability, and hydrological forecasting demand further progress of remotely sensed LAI towards higher accuracy, higher spatial–temporal resolution, and enhanced continuity.

This Special Issue calls for recent advances in the science and technology of using remote sensing to estimate LAI. Topics include but are not limited to: proximal/ground measurements, radiative transfer modeling and theoretical formulation, exploitation of emerging platforms such as UAV and SmallSat, utilization of optical/hyperspectral/LiDAR images, multi-source data fusion, novel machine/deep learning techniques, hybrid modeling, uncertainty quantification, and product development/description/validation. Review and commentary papers are also welcome.

Dr. Yanghui Kang
Dr. Feng Gao
Dr. Rasmus Houborg
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • leaf area index
  • remote sensing
  • in situ sensor
  • radiative transfer modeling
  • LiDAR
  • UAV
  • smallsat
  • data fusion
  • machine learning
  • validation
  • uncertainty

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Published Papers (3 papers)

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Research

20 pages, 5301 KiB  
Article
Characterizing Canopy Structure Variability in Amazonian Secondary Successions with Full-Waveform Airborne LiDAR
by Aline D. Jacon, Lênio Soares Galvão, Rorai Pereira Martins-Neto, Pablo Crespo-Peremarch, Luiz E. O. C. Aragão, Jean P. Ometto, Liana O. Anderson, Laura Barbosa Vedovato, Celso H. L. Silva-Junior, Aline Pontes Lopes, Vinícius Peripato, Mauro Assis, Francisca R. S. Pereira, Isadora Haddad, Catherine Torres de Almeida, Henrique L. G. Cassol and Ricardo Dalagnol
Remote Sens. 2024, 16(12), 2085; https://doi.org/10.3390/rs16122085 - 9 Jun 2024
Cited by 2 | Viewed by 4445
Abstract
Full-waveform LiDAR (FWF) offers a promising advantage over other technologies to represent the vertical canopy structure of secondary successions in the Amazon region, as the waveform encapsulates the properties of all elements intercepting the emitted beam. In this study, we investigated modifications in [...] Read more.
Full-waveform LiDAR (FWF) offers a promising advantage over other technologies to represent the vertical canopy structure of secondary successions in the Amazon region, as the waveform encapsulates the properties of all elements intercepting the emitted beam. In this study, we investigated modifications in the vertical structure of the Amazonian secondary successions across the vegetation gradient from early to advanced stages of vegetation regrowth. The analysis was performed over two distinct climatic regions (Drier and Wetter), designated using the Maximum Cumulative Water Deficit (MCWD). The study area was covered by 309 sample plots distributed along 25 LiDAR transects. The plots were grouped into three successional stages (early—SS1; intermediate—SS2; advanced—SS3). Mature Forest (MF) was used as a reference of comparison. A total of 14 FWF LiDAR metrics from four categories of analysis (Height, Peaks, Understory and Gaussian Decomposition) were extracted using the Waveform LiDAR for Forestry eXtraction (WoLFeX) software (v1.1.1). In addition to examining the variation in these metrics across different successional stages, we calculated their Relative Recovery (RR) with vegetation regrowth, and evaluated their ability to discriminate successional stages using Random Forest (RF). The results showed significant differences in FWF metrics across the successional stages, and within and between sample plots and regions. The Drier region generally exhibited more pronounced differences between successional stages and lower FWF metric values compared to the Wetter region, mainly in the category of height, peaks, and Gaussian decomposition. Furthermore, the Drier region displayed a lower relative recovery of metrics in the early years of succession, compared to the areas of MF, eventually reaching rates akin to those of the Wetter region as succession progressed. Canopy height metrics such as Waveform distance (WD), and Gaussian Decomposition metrics such as Bottom of canopy (BC), Bottom of canopy distance (BCD) and Canopy distance (CD), related to the height of the lower forest stratum, were the most important attributes in discriminating successional stages in both analyzed regions. However, the Drier region exhibited superior discrimination between successional stages, achieving a weighted F1-score of 0.80 compared to 0.73 in the Wetter region. When comparing the metrics from SS in different stages to MF, our findings underscore that secondary forests achieve substantial relative recovery of FWF metrics within the initial 10 years after land abandonment. Regions with potentially slower relative recovery (e.g., Drier regions) may require longer-term planning to ensure success in providing full potential ecosystem services in the Amazon. Full article
(This article belongs to the Special Issue Retrieving Leaf Area Index Using Remote Sensing)
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18 pages, 5401 KiB  
Article
Potato Leaf Area Index Estimation Using Multi-Sensor Unmanned Aerial Vehicle (UAV) Imagery and Machine Learning
by Tong Yu, Jing Zhou, Jiahao Fan, Yi Wang and Zhou Zhang
Remote Sens. 2023, 15(16), 4108; https://doi.org/10.3390/rs15164108 - 21 Aug 2023
Cited by 7 | Viewed by 2453
Abstract
Potato holds significant importance as a staple food crop worldwide, particularly in addressing the needs of a growing population. Accurate estimation of the potato Leaf Area Index (LAI) plays a crucial role in predicting crop yield and facilitating precise management practices. Leveraging the [...] Read more.
Potato holds significant importance as a staple food crop worldwide, particularly in addressing the needs of a growing population. Accurate estimation of the potato Leaf Area Index (LAI) plays a crucial role in predicting crop yield and facilitating precise management practices. Leveraging the capabilities of UAV platforms, we harnessed their efficiency in capturing multi-source, high-resolution remote sensing data. Our study focused on estimating potato LAI utilizing UAV-based digital red–green–blue (RGB) images, Light Detection and Ranging (LiDAR) points, and hyperspectral images (HSI). From these data sources, we computed four sets of indices and employed them as inputs for four different machine-learning regression models: Support Vector Regression (SVR), Random Forest Regression (RFR), Histogram-based Gradient Boosting Regression Tree (HGBR), and Partial Least-Squares Regression (PLSR). We assessed the accuracy of individual features as well as various combinations of feature levels. Among the three sensors, HSI exhibited the most promising results due to its rich spectral information, surpassing the performance of LiDAR and RGB. Notably, the fusion of multiple features outperformed any single component, with the combination of all features of all sensors achieving the highest R2 value of 0.782. HSI, especially when utilized in calculating vegetation indices, emerged as the most critical feature in the combination experiments. LiDAR played a relatively smaller role in potato LAI estimation compared to HSI and RGB. Additionally, we discovered that the RFR excelled at effectively integrating features. Full article
(This article belongs to the Special Issue Retrieving Leaf Area Index Using Remote Sensing)
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18 pages, 4057 KiB  
Article
Assessing the Prospects of Remote Sensing Maize Leaf Area Index Using UAV-Derived Multi-Spectral Data in Smallholder Farms across the Growing Season
by Siphiwokuhle Buthelezi, Onisimo Mutanga, Mbulisi Sibanda, John Odindi, Alistair D. Clulow, Vimbayi G. P. Chimonyo and Tafadzwanashe Mabhaudhi
Remote Sens. 2023, 15(6), 1597; https://doi.org/10.3390/rs15061597 - 15 Mar 2023
Cited by 14 | Viewed by 4085
Abstract
Maize (Zea Mays) is one of the most valuable food crops in sub-Saharan Africa and is a critical component of local, national and regional economies. Whereas over 50% of maize production in the region is produced by smallholder farmers, spatially explicit [...] Read more.
Maize (Zea Mays) is one of the most valuable food crops in sub-Saharan Africa and is a critical component of local, national and regional economies. Whereas over 50% of maize production in the region is produced by smallholder farmers, spatially explicit information on smallholder farm maize production, which is necessary for optimizing productivity, remains scarce due to a lack of appropriate technologies. Maize leaf area index (LAI) is closely related to and influences its canopy physiological processes, which closely relate to its productivity. Hence, understanding maize LAI is critical in assessing maize crop productivity. Unmanned Aerial Vehicle (UAV) imagery in concert with vegetation indices (VIs) obtained at high spatial resolution provides appropriate technologies for determining maize LAI at a farm scale. Five DJI Matrice 300 UAV images were acquired during the maize growing season, and 57 vegetation indices (VIs) were generated from the derived images. Maize LAI samples were collected across the growing season, a Random Forest (RF) regression ensemble based on UAV spectral data and the collected maize LAI samples was used to estimate maize LAI. The results showed that the optimal stage for estimating maize LAI using UAV-derived VIs in concert with the RF ensemble was during the vegetative stage (V8–V10) with an RMSE of 0.15 and an R2 of 0.91 (RRMSE = 8%). The findings also showed that UAV-derived traditional, red edge-based and new VIs could reliably predict maize LAI across the growing season with an R2 of 0.89–0.93, an RMSE of 0.15–0.65 m2/m2 and an RRMSE of 8.13–19.61%. The blue, red edge and NIR sections of the electromagnetic spectrum were critical in predicting maize LAI. Furthermore, combining traditional, red edge-based and new VIs was useful in attaining high LAI estimation accuracies. These results are a step towards achieving robust, efficient and spatially explicit monitoring frameworks for sub-Saharan African smallholder farm productivity. Full article
(This article belongs to the Special Issue Retrieving Leaf Area Index Using Remote Sensing)
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