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Special Issue "Remote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters"

A special issue of Forests (ISSN 1999-4907).

Deadline for manuscript submissions: 15 April 2018

Special Issue Editors

Guest Editor
Dr. Javier García-Haro

Departament de Termodinamica, Facultat de Fisica, Dr. Moliner, 50, 46100 - Burjassot, Valencia, Spain
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Guest Editor
Prof. Dr. Hongliang Fang

LREIS, Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences, 11A Datun Road, Beijing 100101, China
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Guest Editor
Prof. Dr. Juan Manuel Lopez-Sanchez

DFISTS - IUII, Universidad de Alicante, P.O. Box 99, E-03080 Alicante, Spain
Website | E-Mail
Phone: +34 965909597
Fax: +34 965909750
Interests: polarimetry; interferometry; polarimetric SAR interferometry; agriculture; subsidence

Special Issue Information

Dear Colleagues,

Monitoring of vegetation structure and functioning is critical to modeling terrestrial ecosystems and energy cycles. In particular, leaf area index (LAI) is an important structural property of vegetation used in many land-surface vegetation, climate, and crop production models. Canopy structure (LAI, fCover, plant height and biomass) and biochemical parameters (leaf pigmentation and water content) directly influence the radiative transfer process of sunlight in vegetation, determining the amount of radiation measured by passive sensors in the visible and infrared portions of the electromagnetic spectrum.

Optical remote sensing (RS) methods build relationships exploiting in situ measurements and/or as outputs of physical canopy radiative transfer models. The increased availability of passive (radar and LiDAR) RS data has fostered their use in many applications for analysis of land surface properties and processes, thanks also to their insensitivity to weather conditions and the capability to exploit rich structural and texture information. Data fusion and multi-sensor integration techniques are pressing topics to fully exploit the information conveyed by both optical and microwave bands.

This Special Issue will review the state of the art in the retrieval of LAI and other vegetation parameters and its usage in a wide variety of applications (e.g., ecology, carbon cycle, agriculture, forestry and food security). Articles covering recent research about the following topics are invited for this Special Issue:

         Field methods to measure LAI and other vegetation parameters

         Multiple methods for the retrieval of LAI and other structural parameters (e.g., fCover, plant height, biomass) from satellite and airborne sensors

         Methods to estimate vegetation status, dynamic (FAPAR, GPP, stage/phenology) and condition (e.g., pigmentation, leaf water content, water stress)

         Improvement of radiative transfer models, and input data needed for the retrieval of vegetation parameters.

         LiDAR and microwave Remote Sensing

         Evaluations of recent missions (e.g., Landsat-8, Sentinel-1 and -2) to improve the spatial and temporal resolutions of retrieved maps

         LAI and vegetation parameter retrieval from Unmanned Autonomous Vehicle (UAV)

         Processing of big remote sensing data, e.g., data fusion and multi-sensor data integration techniques

         Calibration/validation activities for LAI maps and other biophysical products

         Environmental applications, e.g. detection and mapping of vegetation diseases and stress condition

         Forest applications, e.g., mapping and monitoring of forest disturbance, degradation and regrowth.

         Agriculture applications, e.g., crop growth cycle and crop condition, yield predictions, precision agriculture.

         Assimilation of remote sensing data with vegetation models for forest and agriculture applications

Review articles covering one or more of these topics

 


Guest Editor

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 papers will be 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. Forests is an international peer-reviewed open access monthly 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 1200 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
  • retrieval of vegetation biophysical parameters
  • Optical and microwave remote sensing
  • calibration/validation field campaigns
  • radiative transfer models
  • vegetation monitoring applications

Published Papers (5 papers)

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Research

Open AccessArticle Forest Structure Estimation from a UAV-Based Photogrammetric Point Cloud in Managed Temperate Coniferous Forests
Forests 2017, 8(9), 343; doi:10.3390/f8090343
Received: 18 August 2017 / Revised: 4 September 2017 / Accepted: 6 September 2017 / Published: 13 September 2017
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Abstract
Here, we investigated the capabilities of a lightweight unmanned aerial vehicle (UAV) photogrammetric point cloud for estimating forest biophysical properties in managed temperate coniferous forests in Japan, and the importance of spectral information for the estimation. We estimated four biophysical properties: stand volume
[...] Read more.
Here, we investigated the capabilities of a lightweight unmanned aerial vehicle (UAV) photogrammetric point cloud for estimating forest biophysical properties in managed temperate coniferous forests in Japan, and the importance of spectral information for the estimation. We estimated four biophysical properties: stand volume (V), Lorey’s mean height (HL), mean height (HA), and max height (HM). We developed three independent variable sets, which included a height variable, a spectral variable, and a combined height and spectral variable. The addition of a dominant tree type to the above data sets was also tested. The model including a height variable and dominant tree type was the best for all biophysical property estimations. The root-mean-square errors (RMSEs) for the best model for V, HL, HA, and HM, were 118.30, 1.13, 1.24, and 1.24, respectively. The model including a height variable alone yielded the second highest accuracy. The respective RMSEs were 131.74, 1.21, 1.31, and 1.32. The model including a spectral variable alone yielded much lower estimation accuracy than that including a height variable. Thus, a lightweight UAV photogrammetric point cloud could accurately estimate forest biophysical properties, and a spectral variable was not necessarily required for the estimation. The dominant tree type improved estimation accuracy. Full article
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Open AccessArticle Individual Tree Detection from Unmanned Aerial Vehicle (UAV) Derived Canopy Height Model in an Open Canopy Mixed Conifer Forest
Forests 2017, 8(9), 340; doi:10.3390/f8090340
Received: 28 July 2017 / Revised: 8 September 2017 / Accepted: 8 September 2017 / Published: 11 September 2017
PDF Full-text (7072 KB) | HTML Full-text | XML Full-text
Abstract
Advances in Unmanned Aerial Vehicle (UAV) technology and data processing capabilities have made it feasible to obtain high-resolution imagery and three dimensional (3D) data which can be used for forest monitoring and assessing tree attributes. This study evaluates the applicability of low consumer
[...] Read more.
Advances in Unmanned Aerial Vehicle (UAV) technology and data processing capabilities have made it feasible to obtain high-resolution imagery and three dimensional (3D) data which can be used for forest monitoring and assessing tree attributes. This study evaluates the applicability of low consumer grade cameras attached to UAVs and structure-from-motion (SfM) algorithm for automatic individual tree detection (ITD) using a local-maxima based algorithm on UAV-derived Canopy Height Models (CHMs). This study was conducted in a private forest at Cache Creek located east of Jackson city, Wyoming. Based on the UAV-imagery, we allocated 30 field plots of 20 m × 20 m. For each plot, the number of trees was counted manually using the UAV-derived orthomosaic for reference. A total of 367 reference trees were counted as part of this study and the algorithm detected 312 trees resulting in an accuracy higher than 85% (F-score of 0.86). Overall, the algorithm missed 55 trees (omission errors), and falsely detected 46 trees (commission errors) resulting in a total count of 358 trees. We further determined the impact of fixed tree window sizes (FWS) and fixed smoothing window sizes (SWS) on the ITD accuracy, and detected an inverse relationship between tree density and FWS. From our results, it can be concluded that ITD can be performed with an acceptable accuracy (F > 0.80) from UAV-derived CHMs in an open canopy forest, and has the potential to supplement future research directed towards estimation of above ground biomass and stem volume from UAV-imagery. Full article
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Open AccessFeature PaperArticle Estimation of Forest Biomass Patterns across Northeast China Based on Allometric Scale Relationship
Forests 2017, 8(8), 288; doi:10.3390/f8080288
Received: 30 June 2017 / Revised: 31 July 2017 / Accepted: 1 August 2017 / Published: 8 August 2017
PDF Full-text (2467 KB) | HTML Full-text | XML Full-text
Abstract
This study develops a modeling framework for utilizing the large footprint LiDAR waveform data from the Geoscience Laser Altimeter System (GLAS) onboard NASA’s Ice, Cloud, and Land Elevation Satellite (ICESat), Moderate Resolution Imaging Spectro-Radiometer (MODIS) imagery, meteorological data, and forest measurements for monitoring
[...] Read more.
This study develops a modeling framework for utilizing the large footprint LiDAR waveform data from the Geoscience Laser Altimeter System (GLAS) onboard NASA’s Ice, Cloud, and Land Elevation Satellite (ICESat), Moderate Resolution Imaging Spectro-Radiometer (MODIS) imagery, meteorological data, and forest measurements for monitoring stocks of total biomass (including aboveground biomass and root biomass). The forest tree height models were separately used according to the artificial neural network (ANN) and the allometric scaling and resource limitation (ASRL) tree height models which can both combine the climate data and satellite data to predict forest tree heights. Based on the allometric approach, the forest aboveground biomass model was developed from the field measured aboveground biomass data and the tree heights derived from two tree height models. Then, the root biomass should scale with the aboveground biomass. To investigate whether this approach is efficient for estimating forest total biomass, we used Northeast China as the object of study. Our results generally proved that the method proposed in this study could be meaningful for forest total biomass estimation (R2 = 0.699, RMSE = 55.86). Full article
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Open AccessArticle Automatic Mapping of Forest Stands Based on Three-Dimensional Point Clouds Derived from Terrestrial Laser-Scanning
Forests 2017, 8(8), 265; doi:10.3390/f8080265
Received: 6 July 2017 / Revised: 20 July 2017 / Accepted: 21 July 2017 / Published: 25 July 2017
Cited by 1 | PDF Full-text (1712 KB) | HTML Full-text | XML Full-text
Abstract
Mapping of exact tree positions can be regarded as a crucial task of field work associated with forest monitoring, especially on intensive research plots. We propose a two-stage density clustering approach for the automatic mapping of tree positions, and an algorithm for automatic
[...] Read more.
Mapping of exact tree positions can be regarded as a crucial task of field work associated with forest monitoring, especially on intensive research plots. We propose a two-stage density clustering approach for the automatic mapping of tree positions, and an algorithm for automatic tree diameter estimates based on terrestrial laser-scanning (TLS) point cloud data sampled under limited sighting conditions. We show that our novel approach is able to detect tree positions in a mixed and vertically structured stand with an overall accuracy of 91.6%, and with omission- and commission error of only 5.7% and 2.7% respectively. Moreover, we were able to reproduce the stand’s diameter in breast height (DBH) distribution, and to estimate single trees DBH with a mean average deviation of ±2.90 cm compared with tape measurements as reference. Full article
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Open AccessArticle Potential and Limits of Retrieving Conifer Leaf Area Index Using Smartphone-Based Method
Forests 2017, 8(6), 217; doi:10.3390/f8060217
Received: 17 May 2017 / Revised: 12 June 2017 / Accepted: 15 June 2017 / Published: 19 June 2017
PDF Full-text (1976 KB) | HTML Full-text | XML Full-text
Abstract
Forest leaf area index (LAI) is a key characteristic affecting a field canopy microclimate. In addition to traditional professional measuring instruments, smartphone-based methods have been used to measure forest LAI. However, when smartphone methods were used to measure conifer forest LAI, very different
[...] Read more.
Forest leaf area index (LAI) is a key characteristic affecting a field canopy microclimate. In addition to traditional professional measuring instruments, smartphone-based methods have been used to measure forest LAI. However, when smartphone methods were used to measure conifer forest LAI, very different performances were obtained depending on whether the smartphone was held at the zenith angle or at a 57.5° angle. To further validate the potential of smartphone sensors for measuring conifer LAI and to find the limits of this method, this paper reports the results of a comparison of two smartphone methods with an LAI-2000 instrument. It is shown that the method with the smartphone oriented vertically upwards always produced better consistency in magnitude with LAI-2000. The bias of the LAI between the smartphone method and the LAI-2000 instrument was explained with regards to four aspects that can affect LAI: gap fraction; leaf projection ratio; sensor field of view (FOV); and viewing zenith angle (VZA). It was concluded that large FOV and large VZA cause the 57.5° method to overestimate the gap fraction and hence underestimate conifer LAI. For the vertically upward method, the bias caused by the overestimated gap fraction is compensated for by an underestimated leaf projection ratio. Full article
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