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Earth Observation in Forest Biophysical/Biochemical Parameter Retrieval-II

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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 2024 | Viewed by 4814

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Special Issue Editors

Dr. Prem Chandra Pandey

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Guest Editor

Center for Environmental Sciences and Engineering, School of Natural Science, Shiv Nadar University, Greater Noida, India
Interests: remote sensing applications in forestry & agriculture; hyperspectral remote sensing
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Prof. Dr. Onisimo Mutanga

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Guest Editor

School of Agricultural, Earth and Environmental Sciences, University of Kwazulu-Natal, Durban, South Africa
Interests: remote sensing; land use; environment; vegetation; hyperspectral remote sensing; ecosystem ecology; spatial analysis; climate change impact analysist; vegetation mapping
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Dr. Prashant Srivastava

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Guest Editor

Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India
Interests: remote sensing and GIS; machine learning; water resources; environmental modelling
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Dr. Mukunda Dev Behera

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Guest Editor

Centre for Oceans, Rivers, Atmosphere and Land Sciences (CORAL) & School of Water Resources, Indian Institute of Technology (IIT), Khargpur 721302, West Bengal, India
Interests: ecological climatology; biophysical variables; spatial biodiversity; forest cover dynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In continuation to earlies issue, this issue is focused on the advancement of remote sensing technologies for forest monitoring.

Vegetation is an assemblage of plant species and the ground cover they provide. It is a broad term, without specific reference to particular taxa, life forms, structure, spatial extent, or any geographical characteristics. Vegetation, 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 vegetation health and sustainability.

Earth Observation aids in the spatiotemporally explicit retrieval of 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 vegetation 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 vegetation monioting and management using advanced remote sensing technologies. This further includes evaluation of different techniques for the retrieval of different biophysical/biochemical parameters using available Earth Observation data- Optical, Hyperspectral, LiDAR, Microwave- or multi-soruce- multi-sensors. We invite you to contribute a research article to this Special Issue on one of the following topics (but not limited to);

Vegetation monitoring: forests, grasslands, urban green cover, wetland vegetation or related theme. Cropland monitoring is also considered with similar paramters and assessment.
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).
Wetlands’ emergent hydrophytes and their mapping, advanced technology.
Forest fire analysis, simulation and modelling using advanced techniques.

Dr. Prem Chandra Pandey
Prof. Dr. Onisimo Mutanga
Dr. Prashant Srivastava
Dr. Mukunda Dev Behera
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

biophysical parameters
biochemical parameters
multi-sensors
LiDAR
UAV-AS-drones
microwave
hyperspectral

Published Papers (4 papers)

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Research

22 pages, 7912 KiB

Open AccessArticle

Underlying Topography Estimation over Forest Using Maximum a Posteriori Inversion with Spaceborne Polarimetric SAR Interferometry

by Xiaoshuai Li, Xiaolei Lv and Zenghui Huang

Remote Sens. 2024, 16(6), 948; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16060948 - 08 Mar 2024

Viewed by 429

Abstract

This paper presents a method for extracting the digital elevation model (DEM) of forested areas from polarimetric interferometric synthetic aperture radar (PolInSAR) data. The method models the ground phase as a Von Mises distribution, with a mean of the topographic phase computed from an external DEM. By combining the prior distribution of the ground phase with the complex Wishart distribution of the observation covariance matrix, we derive the maximum a posterior (MAP) inversion method based on the RVoG model and analyze its Cramer–Rao Lower Bound (CRLB). Furthermore, considering the characteristics of the objective function, this paper introduces a Four-Step Optimization (FSO) method based on gradient optimization, which solves the inefficiency problem caused by exhaustive search in solving ground phase using the MAP method. The method is validated using spaceborne L-band repeat-pass SAOCOM data from a test forest area. The test results for FSO indicate that it is approximately 5.6 times faster than traditional methods without compromising accuracy. Simultaneously, the experimental results demonstrate that the method effectively solves the problem of elevation jumps in DEM inversion when modeling the ground phase with the Gaussian distribution. ICESAT-2 data are used to evaluate the accuracy of the inverted DEM, revealing that our method improves the root mean square error (RMSE) by about 23.6% compared to the traditional methods. Full article

(This article belongs to the Special Issue Earth Observation in Forest Biophysical/Biochemical Parameter Retrieval-II)

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16 pages, 1915 KiB

Open AccessArticle

Evaluating Predictive Models of Tree Foliar Moisture Content for Application to Multispectral UAS Data: A Laboratory Study

by Lauren E. Lad, Wade T. Tinkham, Aaron M. Sparks and Alistair M. S. Smith

Remote Sens. 2023, 15(24), 5703; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15245703 - 12 Dec 2023

Cited by 1 | Viewed by 847

Abstract

Water supply is a critical component of tree physiological health, influencing a tree’s photosynthetic activity and resilience to disturbances. The climatic regions of the western United States are particularly at risk from increasing drought, fire, and pest interactions. Existing methods for quantifying drought stress and a tree’s relative resilience against disturbances mostly use moderate-scale (20–30 m) multispectral satellite sensor data. However, tree water status (i.e., water stress) quantification using sensors like Landsat and Sentinel are error-prone given that the spectral reflectance of pixels are a mixture of the dominant tree canopy, surface vegetation, and soil. Uncrewed aerial systems (UAS) equipped with multispectral sensors could potentially provide individual tree water status. In this study, we assess whether the simulated band equivalent reflectance (BER) of a common UAS optical multispectral sensor can accurately quantify the foliar moisture content and water stress status of individual trees. To achieve this, water was withheld from groups of Douglas-fir and western white pine saplings. Then, measurements of each sapling’s foliar moisture content (FMC) and spectral reflectance were converted to BER of a consumer-grade multispectral camera commonly used on UAS. These bands were used in two classification models and three regression models to develop a best-performing FMC model for predicting either the water status (i.e., drought-stressed or healthy) or the foliar moisture content of each sapling, respectively. Our top-performing models were a logistic regression classification and a multiple linear regression which achieved a classification accuracy of 96.55% and an r² of 82.62, respectively. These FMC models could provide an important tool for investigating tree crown level water stress, as well as drought interactions with other disturbances, and provide land managers with a vital indicator of tree resilience. Full article

(This article belongs to the Special Issue Earth Observation in Forest Biophysical/Biochemical Parameter Retrieval-II)

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23 pages, 8208 KiB

Open AccessArticle

Spatiotemporal Vegetation Variability and Linkage with Snow-Hydroclimatic Factors in Western Himalaya Using Remote Sensing and Google Earth Engine (GEE)

by Dhiraj Kumar Singh, Kamal Kant Singh, George P. Petropoulos, Priestly Shan Boaz, Prince Jain, Sartajvir Singh, Dileep Kumar Gupta and Vishakha Sood

Remote Sens. 2023, 15(21), 5239; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15215239 - 04 Nov 2023

Cited by 1 | Viewed by 1266

Abstract

The mountain systems of the Himalayan regions are changing rapidly due to climatic change at a local and global scale. The Indian Western Himalaya ecosystem (between the tree line and the snow line) is an underappreciated component. Yet, knowledge of vegetation distribution, rates of change, and vegetation interactions with snow-hydroclimatic elements is lacking. The purpose of this study is to investigate the linkage between the spatiotemporal variability of vegetation (i.e., greenness and forest) and related snow-hydroclimatic parameters (i.e., snow cover, land surface temperature, Tropical Rainfall Measuring Mission (TRMM), and Evapotranspiration (ET)) in Himachal Pradesh (HP) Basins (i.e., Beas, Chandra, and Bhaga). Spatiotemporal variability in forest and grassland has been estimated from MODIS land cover product (MCD12Q1) using Google Earth Engine (GEE) for the last 19 years (2001–2019). A significant inter- and intra-annual variation in the forest, grassland, and snow-hydroclimatic factors have been observed during the data period in HP basins (i.e., Beas, Chandra, and Bhaga basin). The analysis demonstrates a significant decrease in the forest cover (214 ha/yr.) at the Beas basin; however, a significant increase in grassland cover is noted at the Beas basin (459 ha/yr.), Chandra (176.9 ha/yr.), and Bhaga basin (9.1 ha/yr.) during the data period. Spatiotemporal forest cover loss and gain in the Beas basin have been observed at ~7504 ha (6.6%) and 1819 ha (1.6%), respectively, from 2001 to 2019. However, loss and gain in grassland cover were observed in 3297 ha (2.9%) and 10,688 ha (9.4%) in the Beas basin, 1453 ha (0.59%) and 3941 ha (1.6%) in the Chandra basin, and 1185 ha (0.92%) and 773 ha (0.60%) in the Bhaga basin, respectively. Further, a strong negative correlation (r = −0.65) has been observed between forest cover and evapotranspiration (ET). However, a strong positive correlation (r = 0.99) has been recorded between grassland cover and ET as compared to other factors. The main outcome of this study in terms of spatiotemporal loss and gain in forest and grassland shows that in the Bhaga basin, very little gain and loss have been observed as compared to the Chandra and Beas basins. The present study findings may provide important aid in the protection and advancement of the knowledge gap of the natural environment and the management of water resources in the HP Basin and other high-mountain regions of the Himalayas. For the first time, this study provides a thorough examination of the spatiotemporal variability of forest and grassland and their interactions with snow-hydroclimatic factors using GEE for Western Himalaya. Full article

(This article belongs to the Special Issue Earth Observation in Forest Biophysical/Biochemical Parameter Retrieval-II)

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22 pages, 3460 KiB

Open AccessArticle

Significant Inverse Influence of Tropical Indian Ocean SST on SIF of Indian Vegetation during the Summer Monsoon Onset Phase

by Roma Varghese, Swadhin K. Behera and Mukunda Dev Behera

Remote Sens. 2023, 15(7), 1756; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15071756 - 24 Mar 2023

Viewed by 1502

Abstract

Sea surface temperature (SST) substantially influences the land climate conditions through the co-variability of multiple climate variables, which in turn affect the structural and functional characteristics of terrestrial vegetation. Our study explored the varying responses of vegetation photosynthesis in India to the SST variations in the tropical Indian Ocean during the summer monsoon. To characterise the terrestrial photosynthetic activity, we used solar-induced chlorophyll fluorescence (SIF). Our results demonstrated a significant negative SST-SIF relationship during the onset phase of the summer monsoon: the SIF anomalies in the northern and central Indian regions decrease when strong warm SST anomalies persist in the tropical Indian Ocean. Further, SIF anomalies increase with cold anomalies of SST. However, the negative SST anomalies in the tropical Indian Ocean are less impactful on SIF anomalies relative to the positive SST anomalies. The observed statistically significant SST–SIF link is feasible through atmospheric teleconnections. During monsoon onset, positive SST anomalies in the tropical Indian Ocean favour weakened monsoon flow, decreasing moisture transport from the ocean to the Indian mainland. The resultant water deficiency, along with the high air temperature, created a stress condition and reduced the photosynthetic rate, thus demonstrating negative SIF anomalies across India. Conversely, negative SST anomalies strengthened monsoon winds in the onset period and increased moisture availability across India. Negative air temperature anomalies also dampen water stress conditions and increased photosynthetic activity, resulting in positive SIF anomalies. The identified SST-SIF relationship would be beneficial to generate a simple framework that aids in the detection of the probable impact on vegetation growth across India associated with the rapidly varying climate conditions in the Indian Ocean. Full article

(This article belongs to the Special Issue Earth Observation in Forest Biophysical/Biochemical Parameter Retrieval-II)

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