Special Issue "Earth Observation in Forest Biophysical/Biochemical Parameter Retrieval"

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

Deadline for manuscript submissions: 30 June 2021.

Special Issue Editors

Dr. Prashant K Srivastava
Website
Guest Editor
Remote Sensing Laboratory, Institute of Environment and Sustainable Development (IESD), Banaras Hindu University, Varanasi 221005, India
Interests: microwave active and passive; optical/IR; hydrology; soil moisture; sensitivity and uncertainty analysis; artificial intelligence; geospatial technology; classification methods; simulation and modelling
Special Issues and Collections in MDPI journals
Dr. Ramandeep Kaur M. Malhi

Guest Editor
Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India
Dr. Mukunda Dev Behera

Guest Editor
Centre for Oceans, Rivers, Atmosphere and Land Sciences (CORAL) & School of Water Resources, Indian Institute of Technology (IIT) Khargpur-721302, W.B., INDIA
Prof. Dr. G. Sandhya Kiran

Guest Editor
Department of Botany, M S University of Baroda, Vadodara, India
Dr. Prem Chandra Pandey
Website SciProfiles
Guest Editor
Center for Environmental Sciences and Engineering, School of Natural Science, Shiv Nadar University, India
Dr. George P. Petropoulos
Website
Guest Editor
Department of Soil and Water Resources, Institute of Industrial and Forage Crops, Hellenic Agricultural Organization “Demeter” (former NAGREF), Directorate General of Agricultural Research, 1, Theofrastou St., 41335 Larisa, Greece
Interests: earth observation; modeling; land surface interactions; soil moisture; evapotrasnpiration; land use/cover mapping & change detection; natural hazards; floods; wildfires; sensitivity analysis; soil vegetation atmosphere transfer modeling; operational products benchmarking
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Forests, which occupy about one-third of the terrestrial surface of Earth, play an indispensable role in monitoring global climate change and ecosystem dynamics. The health of forests has been affected in recent years by various stress factors, such as forest fragmentation and deforestation, biodiversity loss, climate change, invasive species, drought, and unsustainable management. In this situation, quantitative appraisal of biophysical and biochemical parameters is imperative since it can provide a direct assessment and prediction of forest health and sustainability.

Earth Observation aids in the spatiotemporally explicit retrieval of forest biophysical and biochemical parameters in both the optical and microwave domain. This information can be utilized to monitor and forecast short- and long-term changes in forest ecosystems that occur due to different factors. Different methods have been explored to retrieve forest biophysical/biochemical parameters, such as parametric regression (including vegetation indices, shape indices, and spectral transformations), nonparametric regression (including linear and nonlinear machine learning regression algorithms), physically based methods (including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches), and hybrid regression methods (that combine RTM simulations with machine learning regression methods).

This Special Issue will cover the evaluation of different techniques for the retrieval of different biophysical/biochemical parameters using available Earth Observation data. We invite you to contribute a research article to this Special Issue on one of the following topics:

  • retrieval of biophysical parameters, viz. LAI, vegetation water content, height, biomass, DBH, etc.;
  • measurement of biochemical parameters, viz. chlorophyll, pigments, etc.;
  • radiative transfer models in the retrieval of biophysical/biochemical parameters;
  • establishment of relationships between in situ measured biophysical/biochemical parameters and ground-measured data;
  • airborne or spaceborne hyperspectral data in the retrieval of biophysical/biochemical parameters;
  • LiDAR and RADAR remote sensing in the estimation of biophysical variables;
  • relation of biophysical/biochemical parameters to climatic factors;
  • biophysical/biochemical parameter retrieval from an Unmanned Autonomous Vehicle (UAV).

Dr. Prashant K Srivastava
Dr. Ramandeep Kaur M. Malhi
Dr. Mukunda Dev Behera
Prof. Dr. G. Sandhya Kiran
Dr. Prem Chandra Pandey
Dr. George P. Petropoulos
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 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. 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 2200 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.

Published Papers (4 papers)

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Research

Open AccessArticle
Assessing the Performance of ICESat-2/ATLAS Multi-Channel Photon Data for Estimating Ground Topography in Forested Terrain
Remote Sens. 2020, 12(13), 2084; https://doi.org/10.3390/rs12132084 - 29 Jun 2020
Abstract
As a continuation of Ice, Cloud, and Land Elevation Satellite-1 (ICESat-1), the ICESat-2/Advanced Topographic Laser Altimeter System (ATLAS) employs a micro-pulse multi-beam photon counting approach to produce photon data for measuring global terrain. Few studies have assessed the accuracy of different ATLAS channels [...] Read more.
As a continuation of Ice, Cloud, and Land Elevation Satellite-1 (ICESat-1), the ICESat-2/Advanced Topographic Laser Altimeter System (ATLAS) employs a micro-pulse multi-beam photon counting approach to produce photon data for measuring global terrain. Few studies have assessed the accuracy of different ATLAS channels in retrieving ground topography in forested terrain. This study aims to assess the accuracy of measuring ground topography in forested terrain using different ATLAS channels and the correlation between laser intensity parameters, laser pointing angle parameters, and elevation error. The accuracy of ground topography measured by the ATLAS footprints is evaluated by comparing the derived Digital Terrain Model (DTM) from the ATL03 (Global Geolocated Photon Data) and ATL08 (Land and Vegetation Height) products with that from the airborne Light Detection And Ranging (LiDAR). Results show that the ATLAS product performed well in the study area at all laser intensities and laser pointing angles, and correlations were found between the ATLAS DTM and airborne LiDAR DTM (coefficient of determination––R2 = 1.00, root mean squared error––RMSE = 0.75 m). Considering different laser intensities, there is a significant correlation between the tx_pulse_energy parameter and elevation error. With different laser pointing angles, there is no significant correlation between the tx_pulse_skew_est, tx_pulse_width_lower, tx_pulse_width_upper parameters and the elevation error. Full article
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Open AccessArticle
Canopy Height Estimation Using Sentinel Series Images through Machine Learning Models in a Mangrove Forest
Remote Sens. 2020, 12(9), 1519; https://doi.org/10.3390/rs12091519 - 09 May 2020
Abstract
Canopy height serves as a good indicator of forest carbon content. Remote sensing-based direct estimations of canopy height are usually based on Light Detection and Ranging (LiDAR) or Synthetic Aperture Radar (SAR) interferometric data. LiDAR data is scarcely available for the Indian tropics, [...] Read more.
Canopy height serves as a good indicator of forest carbon content. Remote sensing-based direct estimations of canopy height are usually based on Light Detection and Ranging (LiDAR) or Synthetic Aperture Radar (SAR) interferometric data. LiDAR data is scarcely available for the Indian tropics, while Interferometric SAR data from commercial satellites are costly. High temporal decorrelation makes freely available Sentinel-1 interferometric data mostly unsuitable for tropical forests. Alternatively, other remote sensing and biophysical parameters have shown good correlation with forest canopy height. The study objective was to establish and validate a methodology by which forest canopy height can be estimated from SAR and optical remote sensing data using machine learning models i.e., Random Forest (RF) and Symbolic Regression (SR). Here, we analysed the potential of Sentinel-1 interferometric coherence and Sentinel-2 biophysical parameters to propose a new method for estimating canopy height in the study site of the Bhitarkanika wildlife sanctuary, which has mangrove forests. The results showed that interferometric coherence, and biophysical variables (Leaf Area Index (LAI) and Fraction of Vegetation Cover (FVC)) have reasonable correlation with canopy height. The RF model showed a Root Mean Squared Error (RMSE) of 1.57 m and R2 value of 0.60 between observed and predicted canopy heights; whereas, the SR model through genetic programming demonstrated better RMSE and R2 values of 1.48 and 0.62 m, respectively. The SR also established an interpretable model, which is not possible via any other machine learning algorithms. The FVC was found to be an essential variable for predicting forest canopy height. The canopy height maps correlated with ICESat-2 estimated canopy height, albeit modestly. The study demonstrated the effectiveness of Sentinel series data and the machine learning models in predicting canopy height. Therefore, in the absence of commercial and rare data sources, the methodology demonstrated here offers a plausible alternative for forest canopy height estimation. Full article
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Open AccessArticle
Forest Height Estimation Based on P-Band Pol-InSAR Modeling and Multi-Baseline Inversion
Remote Sens. 2020, 12(8), 1319; https://doi.org/10.3390/rs12081319 - 22 Apr 2020
Abstract
The Gaussian vertical backscatter (GVB) model has a pivotal role in describing the forest vertical structure more accurately, which is reflected by P-band polarimetric interferometric synthetic aperture radar (Pol-InSAR) with strong penetrability. The model uses a three-dimensional parameter space (forest height, Gaussian mean [...] Read more.
The Gaussian vertical backscatter (GVB) model has a pivotal role in describing the forest vertical structure more accurately, which is reflected by P-band polarimetric interferometric synthetic aperture radar (Pol-InSAR) with strong penetrability. The model uses a three-dimensional parameter space (forest height, Gaussian mean representing the strongest backscattered power elevation, and the corresponding standard deviation) to interpret the forest vertical structure. This paper establishes a two-dimensional GVB model by simplifying the three-dimensional one. Specifically, the two-dimensional GVB model includes the following three cases: the Gaussian mean is located at the bottom of the canopy, the Gaussian mean is located at the top of the canopy, as well as a constant volume profile. In the first two cases, only the forest height and the Gaussian standard deviation are variable. The above approximation operation generates a two-dimensional volume only coherence solution space on the complex plane. Based on the established two-dimensional GVB model, the three-baseline inversion is achieved without the null ground-to-volume ratio assumption. The proposed method improves the performance by 18.62% compared to the three-baseline Random Volume over Ground (RVoG) model inversion. In particular, in the area where the radar incidence angle is less than 0.6 rad, the proposed method improves the inversion accuracy by 34.71%. It suggests that the two-dimensional GVB model reduces the GVB model complexity while maintaining a strong description ability. Full article
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Open AccessArticle
Use of Hyperion for Mangrove Forest Carbon Stock Assessment in Bhitarkanika Forest Reserve: A Contribution Towards Blue Carbon Initiative
Remote Sens. 2020, 12(4), 597; https://doi.org/10.3390/rs12040597 - 11 Feb 2020
Abstract
Mangrove forest coastal ecosystems contain significant amount of carbon stocks and contribute to approximately 15% of the total carbon sequestered in ocean sediments. The present study aims at exploring the ability of Earth Observation EO-1 Hyperion hyperspectral sensor in estimating aboveground carbon stocks [...] Read more.
Mangrove forest coastal ecosystems contain significant amount of carbon stocks and contribute to approximately 15% of the total carbon sequestered in ocean sediments. The present study aims at exploring the ability of Earth Observation EO-1 Hyperion hyperspectral sensor in estimating aboveground carbon stocks in mangrove forests. Bhitarkanika mangrove forest has been used as case study, where field measurements of the biomass and carbon were acquired simultaneously with the satellite data. The spatial distribution of most dominant mangrove species was identified using the Spectral Angle Mapper (SAM) classifier, which was implemented using the spectral profiles extracted from the hyperspectral data. SAM performed well, identifying the total area that each of the major species covers (overall kappa = 0.81). From the hyperspectral images, the NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) were applied to assess the carbon stocks of the various species using machine learning (Linear, Polynomial, Logarithmic, Radial Basis Function (RBF), and Sigmoidal Function) models. NDVI and EVI is generated using covariance matrix based band selection algorithm. All the five machine learning models were tested between the carbon measured in the field sampling and the carbon estimated by the vegetation indices NDVI and EVI was satisfactory (Pearson correlation coefficient, R, of 86.98% for EVI and of 84.1% for NDVI), with the RBF model showing the best results in comparison to other models. As such, the aboveground carbon stocks for species-wise mangrove for the study area was estimated. Our study findings confirm that hyperspectral images such as those from Hyperion can be used to perform species-wise mangrove analysis and assess the carbon stocks with satisfactory accuracy. Full article
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