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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: closed (30 September 2023) | Viewed by 13678

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Special Issue Editor

Prof. Dr. Arturo Sanchez-Azofeifa

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Guest Editor

Earth and Atmospheric Sciences Department, University of Alberta, Edmonton, AB T6G 2R3, Canada
Interests: multi and hyper-spectral remote sensing; ecosystem succession; time series trend-analysis; geostatistics; spatial modeling; wireless sensor networks
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Special Issue Information

Dear Colleagues,

I am pleased to invite all Editorial Boards Members in the Remote Sensing to contribute their works to this Special Issue of Remote Sensing, which will showcase new insights, novel developments, current challenges, the latest discoveries, recent advances, and future perspectives in the field of Forest Remote Sensing. We are planning a comprehensive Special Issue comprising contributions from our Editorial Boards Members considering topics associated with passive and active remote sensing. Specifically, we are seeking contributions on the use of Radar, multi- and hyperspectral remote sensing, terrestrial, airborne and spaceborne laser scanning, and near-surface remote sensing (drones, wireless sensor networks). Articles linking remote sensing observations to novel biodiversity measurements, biomass estimation, phenology and land use/land cover change and machine learning/artificial intelligence applied to forestry issues are welcome.

The Special Issue is only collecting manuscripts invited by the Editorial Office and Editorial Board Members. Articles authored or co-authored by our Editorial Board Members are welcome, and the article processing charges of the papers in this collection will be waived.

Please do not hesitate to contact Ms. Nancy Yang (nancy.yang@mdpi.com) if you require additional information.

Prof. Dr. Arturo Sanchez-Azofeifa
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 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.

Published Papers (5 papers)

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Research

26 pages, 5748 KiB

Open AccessArticle

Discrimination of Leaves in a Multi-Layered Mediterranean Forest through Machine Learning Algorithms

by Cesar Alvites, Mauro Maesano, Juan Alberto Molina-Valero, Bruno Lasserre, Marco Marchetti and Giovanni Santopuoli

Remote Sens. 2023, 15(18), 4450; https://doi.org/10.3390/rs15184450 - 10 Sep 2023

Viewed by 932

Abstract

Terrestrial laser scanning (TLS) technology characterizes standing trees with millimetric precision. An important step to accurately quantify tree volume and above-ground biomass using TLS point clouds is the discrimination between timber and leaf components. This study evaluates the performance of machine learning (ML)-derived models aimed at discriminating timber and leaf TLS point clouds, focusing on eight Mediterranean tree species datasets. The results show the best accuracies for random forests, gradient boosting machine, stacked ensemble model, and deep learning models with an average F1 score equal to 0.92. The top-performing ML-derived models showed well-balanced average precision and recall rates, ranging from 0.86 to 0.91 and 0.92 to 0.96 for precision and recall, respectively. Our findings show that Italian maple, European beech, hazel, and small-leaf lime tree species have more accurate F1 scores, with the best average F1 score of 0.96. The factors influencing the timber–leaf discrimination include phenotypic factors, such as bark surface (i.e., roughness and smoothness), technical issues (i.e., noise points and misclassification of points), and secondary factors (i.e., bark defects, lianas, and microhabitats). The top-performing ML-derived models report a time computation ranging from 8 to 37 s for processing 2 million points. Future studies are encouraged to calibrate, configure, and validate the potential of top-performing ML-derived models on other tree species and at the plot level. Full article

(This article belongs to the Special Issue New Advancements in the Field of Forest Remote Sensing)

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19 pages, 4249 KiB

Open AccessArticle

Characterizing Transitions between Successional Stages in a Tropical Dry Forest Using LiDAR Techniques

by Menglei Duan, Connor Bax, Kati Laakso, Nooshin Mashhadi, Nelson Mattie and Arturo Sanchez-Azofeifa

Remote Sens. 2023, 15(2), 479; https://doi.org/10.3390/rs15020479 - 13 Jan 2023

Cited by 5 | Viewed by 1800

Abstract

Secondary succession is defined as natural regeneration following complete forest clearance from anthropogenic or natural disturbances. Traditional strategies aimed to map and characterize secondary succession using remote sensing are usually based on deterministic approaches, where transitions between successional stages are not considered. These transitions represent rich environments between successional stages and play a key role in ecosystem regeneration. Here, we evaluate the use of the Full-waveform Airborne LiDAR to characterize changes in forest structure between the transition of early-to-intermediate and intermediate-to-late forest succession at the Santa Rosa National Park Environmental Monitoring Super Site (SRNP-EMSS), Guanacaste, Costa Rica. The vertical forest structure was analyzed on twenty cross-sections selected between forest transitions previously mapped using machine learning; leaf area density (LAD) and waveform metrics were studied based on the waveform profile derived from twenty-seven plots distributed in different successional forest patches. Results suggest that LiDAR techniques can identify forest structure differences between successional stages and their transitions. The significance proves that transitions exist, highlights the unique transitional characteristics between intermediate and late successional stages and contributes to understanding the significance of inter-successional stages (transitions) in secondary dry forests. Full article

(This article belongs to the Special Issue New Advancements in the Field of Forest Remote Sensing)

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15 pages, 5722 KiB

Open AccessArticle

Modelling Species Richness and Functional Diversity in Tropical Dry Forests Using Multispectral Remotely Sensed and Topographic Data

by Víctor Alexis Peña-Lara, Juan Manuel Dupuy, Casandra Reyes-Garcia, Lucia Sanaphre-Villanueva, Carlos A. Portillo-Quintero and José Luis Hernández-Stefanoni

Remote Sens. 2022, 14(23), 5919; https://doi.org/10.3390/rs14235919 - 23 Nov 2022

Cited by 4 | Viewed by 3414

Abstract

Efforts to assess and understand changes in plant diversity and ecosystem functioning focus on the analysis of taxonomic diversity. However, the resilience of ecosystems depends not only on species richness but also on the functions (responses and effects) of species within communities and ecosystems. Therefore, a functional approach is required to estimate functional diversity through functional traits and to model its changes in space and time. This study aims to: (i) assess the accuracy of estimates of species richness and tree functional richness obtained from field data and Sentinel-2 imagery in tropical dry forests of the Yucatan Peninsula; (ii) map and analyze the relationships between these two variables. We calculated species richness and functional richness (from six functional traits) of trees from 87 plots of the National Forest Inventory in a semi-deciduous tropical forest and 107 in a semi-evergreen tropical forest. Species richness and functional richness were mapped using reflectance values, vegetation indices, and texture measurements from Sentinel-2 imagery as explanatory variables. Validation of the models to map these two variables yielded a coefficient of determination (R²) of 0.43 and 0.50, and a mean squared relative error of 25.4% and 48.8%, for tree species richness and functional richness, respectively. For both response variables, the most important explanatory variables were Sentinel-2 texture measurements and spectral bands. Tree species richness and functional richness were positively correlated in both forest types. Bivariate maps showed that 44.9% and 26.5% of the forests studied had high species richness and functional richness values. Our findings highlight the importance of integrating field data and remotely sensed variables for estimating tree species richness and functional richness. In addition, the combination of species richness and functional richness maps presented here is potentially valuable for planning, conservation, and restoration strategies by identifying areas that maximize ecosystem service provisioning, carbon storage, and biodiversity conservation. Full article

(This article belongs to the Special Issue New Advancements in the Field of Forest Remote Sensing)

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21 pages, 6210 KiB

Open AccessArticle

Calibration of Co-Located Identical PAR Sensors Using Wireless Sensor Networks and Characterization of the In Situ fPAR Variability in a Tropical Dry Forest

by Arturo Sanchez-Azofeifa, Iain Sharp, Paul D. Green and Joanne Nightingale

Remote Sens. 2022, 14(12), 2752; https://doi.org/10.3390/rs14122752 - 08 Jun 2022

Cited by 1 | Viewed by 1752

Abstract

The fraction of photosynthetic active radiation (fPAR) attempts to quantify the amount of enery that is absorbed by vegetation for use in photosynthesis. Despite the importance of fPAR, there has been little research into how fPAR may change with biome and latitude, or the extent and number of ground networks required to validate satellite products. This study provides the first attempt to quantify the variability and uncertainties related to in-situ 2-flux fPAR estimation within a tropical dry forest (TDF) via co-located sensors. Using the wireless sensor network (WSN) at the Santa Rosa National Park Environmental Monitoring Super Site (Guanacaste, Costa Rica), this study analyzes the 2-flux fPAR response to seasonal, environmental, and meteorological influences over a period of five years (2013–2017). Using statistical tests on the distribution of fPAR measurements throughout the days and seasons based on the sky condition, solar zenith angle, and wind-speed, we determine which conditions reduce variability, and their relative impact on in-situ fPAR estimation. Additionally, using a generalized linear mixed effects model, we determine the relative impact of the factors above, as well as soil moisture on the prediction of fPAR. Our findings suggest that broadleaf deciduous forests, diffuse light conditions, and low wind patterns reduce variability in fPAR, whereas higher winds and direct sunlight increase variability between co-located sensors. The co-located sensors used in this study were found to agree within uncertanties; however, this uncertainty is dominated by the sensor drift term, requiring routine recalibration of the sensor to remain within a defined criteria. We found that for the Apogee SQ-110 sensor using the manufacturer calibration, recalibration around every 4 years is needed to ensure that it remains within the 10% global climate observation system (GCOS) requirement. We finally also find that soil moisture is a significant predictor of the distribution and magnitude of fPAR, and particularly impacts the onset of senescence for TDFs. Full article

(This article belongs to the Special Issue New Advancements in the Field of Forest Remote Sensing)

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28 pages, 23198 KiB

Open AccessArticle

Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine

by Pablo Reyes-Muñoz, Luca Pipia, Matías Salinero-Delgado, Santiago Belda, Katja Berger, José Estévez, Miguel Morata, Juan Pablo Rivera-Caicedo and Jochem Verrelst

Remote Sens. 2022, 14(6), 1347; https://doi.org/10.3390/rs14061347 - 10 Mar 2022

Cited by 16 | Viewed by 4411

Abstract

Thanks to the emergence of cloud-computing platforms and the ability of machine learning methods to solve prediction problems efficiently, this work presents a workflow to automate spatiotemporal mapping of essential vegetation traits from Sentinel-3 (S3) imagery. The traits included leaf chlorophyll content (LCC), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fractional vegetation cover (FVC), being fundamental for assessing photosynthetic activity on Earth. The workflow involved Gaussian process regression (GPR) algorithms trained on top-of-atmosphere (TOA) radiance simulations generated by the coupled canopy radiative transfer model (RTM) SCOPE and the atmospheric RTM 6SV. The retrieval models, named to S3-TOA-GPR-1.0, were directly implemented in Google Earth Engine (GEE) to enable the quantification of the traits from TOA data as acquired from the S3 Ocean and Land Colour Instrument (OLCI) sensor. Following good to high theoretical validation results with normalized root mean square error (NRMSE) ranging from 5% (FAPAR) to 19% (LAI), a three fold evaluation approach over diverse sites and land cover types was pursued: (1) temporal comparison against LAI and FAPAR products obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) for the time window 2016–2020, (2) spatial difference mapping with Copernicus Global Land Service (CGLS) estimates, and (3) direct validation using interpolated in situ data from the VALERI network. For all three approaches, promising results were achieved. Selected sites demonstrated coherent seasonal patterns compared to LAI and FAPAR MODIS products, with differences between spatially averaged temporal patterns of only 6.59%. In respect of the spatial mapping comparison, estimates provided by the S3-TOA-GPR-1.0 models indicated highest consistency with FVC and FAPAR CGLS products. Moreover, the direct validation of our S3-TOA-GPR-1.0 models against VALERI estimates indicated good retrieval performance for LAI, FAPAR and FVC. We conclude that our retrieval workflow of spatiotemporal S3 TOA data processing into GEE opens the path towards global monitoring of fundamental vegetation traits, accessible to the whole research community. Full article

(This article belongs to the Special Issue New Advancements in the Field of Forest Remote Sensing)

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