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Advances in Vegetation Structure Modelling to Support the Sustainable Development Goals Acquisition through Forest Management

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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 (31 May 2023) | Viewed by 26975

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Special Issue Editors

Dr. María Teresa Lamelas

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Guest Editor

Departamento de Geografía y Ordenación del Territorio, Centro Universitario de la Defensa Zaragoza, 50090 Zaragoza, Spain
Interests: resources and hazards modeling through remote sensing and GIS; land management; LiDAR and multispectral remote sensing; forest management; 3D vegetation structure characterization

Dr. Dario Domingo

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Guest Editor

EiFAB-iuFOR, University of Valladolid, Campus Duques de Soria, 42004 Soria, Spain
Interests: spatial analysis and environmental modelling; passive remote sensing and LiDAR; forest management; land change modelling and spatial planning

Special Issue Information

Dear Colleagues,

The end of this decade represents a new horizon, with pressing challenges for human beings in coming years. Caring for the environment by combating climate change and protecting terrestrial ecosystems are two of the sustainable development goals set out to be achieved in the 2030 Agenda. The achievement of these goals depends directly on sustainable forest management. The development of LiDAR technology, especially airborne laser scanning (ALS), constitutes an important advance in forest management made through remote sensing techniques, due to the possibility of capturing the vegetation’s vertical profile. In recent years, complementary technologies, such as photogrammetry, have arisen to improve vegetation structure modeling due to the availability of LiDAR data from satellite missions such as the Global Ecosystem Dynamics Investigation LiDAR (GEDI) or the Ice, Cloud and Land Elevation Satellite-2 (or ICESat-2). These technological developments have been accompanied by advances in modeling methods, from empirical (parametric and non-parametric statistical approaches) to physical methods, such as Radiative Transfer Models (RTM), especially 3D RTM, which is capable of simulating the LiDAR response.

This Special Issue is aimed at studies covering the application of advanced remote sensing techniques to vegetation structure modeling, with the aim of supporting sustainable forest management. Topics may cover the wide range of variables related to resources and hazard modeling and mapping, such as:

Wildfire—fuel parameters, fuel model, etc.;
Dasometry/inventory—height, density, volume, etc.;
Climate change—carbon stock, CO₂ emissions;
Ecology—biodiversity, pattern analysis, conservation state.

Dr. María Teresa Lamelas
Dr. Dario Domingo
Guest Editors

Manuscript Submission Information

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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

Vegetation structure
Forest fuels
Forest inventory
Carbon stock
Biodiversity
LiDAR
Photogrammetry
Empirical modelling
RTM

Published Papers (10 papers)

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Editorial

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4 pages, 212 KiB

Open AccessEditorial

Advances in Vegetation Structure Modelling Using Remote Sensing to Support the Acquisition of Sustainable Development Goals through Forest Management

by María Teresa Lamelas and Darío Domingo

Remote Sens. 2023, 15(18), 4589; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15184589 - 18 Sep 2023

Viewed by 624

Abstract

Forest ecosystems cover 31% of the world [...] Full article

(This article belongs to the Special Issue Advances in Vegetation Structure Modelling to Support the Sustainable Development Goals Acquisition through Forest Management)

Research

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27 pages, 47764 KiB

Open AccessArticle

Forest Structure Characterization in Germany: Novel Products and Analysis Based on GEDI, Sentinel-1 and Sentinel-2 Data

by Patrick Kacic, Frank Thonfeld, Ursula Gessner and Claudia Kuenzer

Remote Sens. 2023, 15(8), 1969; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15081969 - 07 Apr 2023

Cited by 8 | Viewed by 3350

Abstract

Monitoring forest conditions is an essential task in the context of global climate change to preserve biodiversity, protect carbon sinks and foster future forest resilience. Severe impacts of heatwaves and droughts triggering cascading effects such as insect infestation are challenging the semi-natural forests in Germany. As a consequence of repeated drought years since 2018, large-scale canopy cover loss has occurred calling for an improved disturbance monitoring and assessment of forest structure conditions. The present study demonstrates the potential of complementary remote sensing sensors to generate wall-to-wall products of forest structure for Germany. The combination of high spatial and temporal resolution imagery from Sentinel-1 (Synthetic Aperture Radar, SAR) and Sentinel-2 (multispectral) with novel samples on forest structure from the Global Ecosystem Dynamics Investigation (GEDI, LiDAR, Light detection and ranging) enables the analysis of forest structure dynamics. Modeling the three-dimensional structure of forests from GEDI samples in machine learning models reveals the recent changes in German forests due to disturbances (e.g., canopy cover degradation, salvage logging). This first consistent data set on forest structure for Germany from 2017 to 2022 provides information of forest canopy height, forest canopy cover and forest biomass and allows estimating recent forest conditions at 10 m spatial resolution. The wall-to-wall maps of the forest structure support a better understanding of post-disturbance forest structure and forest resilience. Full article

(This article belongs to the Special Issue Advances in Vegetation Structure Modelling to Support the Sustainable Development Goals Acquisition through Forest Management)

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25 pages, 9423 KiB

Open AccessEditor’s ChoiceArticle

Assessing the Performance of a Handheld Laser Scanning System for Individual Tree Mapping—A Mixed Forests Showcase in Spain

by Frederico Tupinambá-Simões, Adrián Pascual, Juan Guerra-Hernández, Cristóbal Ordóñez, Tiago de Conto and Felipe Bravo

Remote Sens. 2023, 15(5), 1169; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15051169 - 21 Feb 2023

Cited by 8 | Viewed by 3281

Abstract

The use of mobile laser scanning to survey forest ecosystems is a promising, scalable technology to describe the 3D structure of forests at a high resolution. We use a structurally complex, mixed-species Mediterranean forest to test the performance of a mobile Handheld Laser Scanning (HLS) system to estimate tree attributes within a forest patch in central Spain. We describe the different stages of the HLS approach: field position, ground data collection, scanning path design, point cloud processing, alignment between detected trees and measured reference trees, and finally, the assessment of main tree structural attributes diameter at breast height (DBH) and tree height considering species and tree size as control factors. We surveyed 418 reference trees to account for omission and commission error rates over a 1 ha plot divided into 16 sections and scanned using two different scanning paths. The HLS-based approach reached a high of 88 and 92% tree detection rate for the best combination of scanning path and point cloud processing modes for the HLS system. The root mean squared errors for DBH estimates varied between species: errors for Pinus pinaster were below 2 cm for Scan 02. Quercus pyrenaica, and Alnus glutinosa showed higher error rates. We observed good agreement between ALS and HLS estimates for tree height, highlighting differences to field measurements. Despite the complexity of the mixed forest area surveyed, our results show that HLS is highly efficient at detecting tree locations, estimating DBH, and supporting tree height measurements as confirmed with airborne laser data used for validation. This study is one of the first HLS-based studies conducted in the Mediterranean mixed forest region, where variability in tree allometries and spacing and the presence of natural regeneration pose challenges for the HLS approach. HLS is a feasible, time-efficient, scalable technology for tree mapping in mixed forests with potential to support forest monitoring programmes such as national forest inventories lacking three-dimensional, remote sensing data to support field measurements. Full article

(This article belongs to the Special Issue Advances in Vegetation Structure Modelling to Support the Sustainable Development Goals Acquisition through Forest Management)

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17 pages, 1870 KiB

Open AccessFeature PaperArticle

Assessment of Oak Groves Conservation Statuses in Natura 2000 Sacs with Single Photon Lidar and Sentinel-2 Data

by Aitor García-Galar, M. Teresa Lamelas and Darío Domingo

Remote Sens. 2023, 15(3), 710; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15030710 - 25 Jan 2023

Cited by 2 | Viewed by 1670

Abstract

Among the main objectives of Natura 2000 Network sites management plans is monitoring their conservation status under a reasonable cost and with high temporal frequency. The aim of this study is to assess the ability of single-photon light detection and ranging (LiDAR) technology (14 points per m²) and Sentinel-2 data to classify the conservation status of oak forests in four special areas of conservation in Navarra Province (Spain) that comprise three habitats. To capture the variability of conservation status within the three habitats, we first performed a random stratified sampling based on conservation status measured in the field, canopy cover, and terrain slope and height. Thereafter, we compared two metric selection approaches, namely Kruskal–Wallis and Dunn tests, and two machine learning classification methods, random forest (RF) and support vector machine (SVM), to classify the conservation statuses using LiDAR and Sentinel-2 data. The best-fit classification model, which included only LiDAR metrics, was obtained using the random forest method, with an overall classification accuracy after validation of 83.01%, 75.51%, and 88.25% for Quercus robur (9160), Quercus pyrenaica (9230), and Quercus faginea (9240) habitats, respectively. The models include three to six LiDAR metrics, with the structural diversity indices (LiDAR height evenness index, LHEI, and LiDAR height diversity index, LHDI) and canopy cover (FCC) being the most relevant ones. The inclusion of the NDVI index from the Sentinel-2 image did not improve the classification accuracy significantly. This approach demonstrates its value for classifying and subsequently mapping conservation statuses in oak groves and other Natura 2000 Network habitat sites at a regional scale, which could serve for more effective monitoring and management of high biodiversity habitats. Full article

(This article belongs to the Special Issue Advances in Vegetation Structure Modelling to Support the Sustainable Development Goals Acquisition through Forest Management)

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24 pages, 4618 KiB

Open AccessArticle

A Novel Method for Detecting and Delineating Coppice Trees in UAV Images to Monitor Tree Decline

by Marziye Ghasemi, Hooman Latifi and Mehdi Pourhashemi

Remote Sens. 2022, 14(23), 5910; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14235910 - 22 Nov 2022

Cited by 8 | Viewed by 2268

Abstract

Monitoring tree decline in arid and semi-arid zones requires methods that can provide up-to-date and accurate information on the health status of the trees at single-tree and sample plot levels. Unmanned Aerial Vehicles (UAVs) are considered as cost-effective and efficient tools to study tree structure and health at small scale, on which detecting and delineating tree crowns is the first step to extracting varied subsequent information. However, one of the major challenges in broadleaved tree cover is still detecting and delineating tree crowns in images. The frequent dominance of coppice structure in degraded semi-arid vegetation exacerbates this problem. Here, we present a new method based on edge detection for delineating tree crowns based on the features of oak trees in semi-arid coppice structures. The decline severity in individual stands can be analyzed by extracting relevant information such as texture from the crown area. Although the method presented in this study is not fully automated, it returned high performances including an F-score = 0.91. Associating the texture indices calculated in the canopy area with the phenotypic decline index suggested higher correlations of the GLCM texture indices with tree decline at the tree level and hence a high potential to be used for subsequent remote-sensing-assisted tree decline studies. Full article

(This article belongs to the Special Issue Advances in Vegetation Structure Modelling to Support the Sustainable Development Goals Acquisition through Forest Management)

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15 pages, 3842 KiB

Open AccessArticle

Stand Structural Characteristics Derived from Combined TLS and Landsat Data Support Predictions of Mushroom Yields in Mediterranean Forest

by Raquel Martínez-Rodrigo, Cristina Gómez, Astor Toraño-Caicoya, Luke Bohnhorst, Enno Uhl and Beatriz Águeda

Remote Sens. 2022, 14(19), 5025; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14195025 - 09 Oct 2022

Cited by 2 | Viewed by 1535

Abstract

Forest fungi provide recreational and economic services, as well as ecosystem biodiversity. Wild mushroom yields are difficult to estimate; climatic conditions are known to trigger temporally localised yields, and forest structure also affects productivity. In this work, we analyse the capacity of remotely sensed variables to estimate wild mushroom biomass production in Mediterranean Pinus pinaster forests in Soria (Spain) using generalised additive mixed models (GAMMs). In addition to climate variables, multitemporal NDVI derived from Landsat data, as well as structural variables measured with mobile Terrestrial Laser Scanner (TLS), are considered. Models are built for all mushroom species as a single pool and for Lactarius deliciosus individually. Our results show that, in addition to autumn precipitation, the interaction of multitemporal NDVI and vegetation biomass are most explanatory of mushroom productivity in the models. When analysing the productivity models of Lactarius deliciosus, in addition to the interaction between canopy cover and autumn minimum temperature, basal area (BA) becomes relevant, indicating an optimal BA range for the development of this species. These findings contribute to the improvement of knowledge about wild mushroom productivity, helping to meet Goal 15 of the 2030 UN Agenda. Full article

(This article belongs to the Special Issue Advances in Vegetation Structure Modelling to Support the Sustainable Development Goals Acquisition through Forest Management)

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23 pages, 6539 KiB

Open AccessArticle

Identifying the Factors behind Climate Diversification and Refugial Capacity in Mountain Landscapes: The Key Role of Forests

by Raúl Hoffrén, Héctor Miranda, Manuel Pizarro, Pablo Tejero and María B. García

Remote Sens. 2022, 14(7), 1708; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071708 - 01 Apr 2022

Cited by 5 | Viewed by 2135

Abstract

Recent studies have shown the importance of small-scale climate diversification and climate microrefugia for organisms to escape or suffer less from the impact of current climate change. These situations are common in topographically complex terrains like mountains, where many climate-forcing factors vary at a fine spatial resolution. We investigated this effect in a high roughness area of a southern European range (the Pyrenees), with the aid of a network of miniaturized temperature and relative humidity sensors distributed across 2100 m of elevation difference. We modeled the minimum (Tn) and maximum (Tx) temperatures above- and below-ground, and maximum vapor pressure deficit (VPDmax), as a function of several topographic and vegetation variables derived from ALS-LiDAR data and Landsat series. Microclimatic models had a good fit, working better in soil than in air, and for Tn than for Tx. Topographic variables (including elevation) had a larger effect on above-ground Tn, and vegetation variables on Tx. Forest canopy had a significant effect not only on the spatial diversity of microclimatic metrics but also on their refugial capacity, either stabilizing thermal ranges or offsetting free-air extreme temperatures and VPDmax. Our integrative approach provided an overview of microclimatic differences between air and soil, forests and open areas, and highlighted the importance of preserving and managing forests to mitigate the impacts of climate change on biodiversity. Remote-sensing can provide essential tools to detect areas that accumulate different factors extensively promoting refugial capacity, which should be prioritized based on their high resilience. Full article

(This article belongs to the Special Issue Advances in Vegetation Structure Modelling to Support the Sustainable Development Goals Acquisition through Forest Management)

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20 pages, 4379 KiB

Open AccessArticle

A Conceptual Model for Detecting Small-Scale Forest Disturbances Based on Ecosystem Morphological Traits

by Jaz Stoddart, Danilo Roberti Alves de Almeida, Carlos Alberto Silva, Eric Bastos Görgens, Michael Keller and Ruben Valbuena

Remote Sens. 2022, 14(4), 933; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14040933 - 15 Feb 2022

Cited by 4 | Viewed by 1866

Abstract

Current LiDAR-based methods for detecting forest change use a host of statistically selected variables which typically lack a biological link with the characteristics of the ecosystem. Consensus of the literature indicates that many authors use LiDAR to derive ecosystem morphological traits (EMTs)—namely, vegetation height, vegetation cover, and vertical structural complexity—to identify small-scale changes in forest ecosystems. Here, we provide a conceptual, biological model for predicting forest aboveground biomass (AGB) change based on EMTs. We show that through use of a multitemporal dataset it is possible to not only identify losses caused by logging in the period between data collection but also identify regions of regrowth from prior logging using EMTs. This sensitivity to the change in forest dynamics was the criterion by which LiDAR metrics were selected as proxies for each EMT. For vegetation height, results showed that the top-of-canopy height derived from a canopy height model was more sensitive to logging than the average or high percentile of raw LiDAR height distributions. For vegetation cover metrics, lower height thresholds for fractional cover calculations were more sensitive to selective logging and the regeneration of understory. For describing the structural complexity in the vertical profile, the Gini coefficient was found to be superior to foliage height diversity for detecting the dynamics occurring over the years after logging. The subsequent conceptual model for AGB estimation obtained a level of accuracy which was comparable to a model that was statistically optimised for that same area. We argue that a widespread adoption of an EMT-based conceptual approach would improve the transferability and comparability of LiDAR models for AGB worldwide. Full article

(This article belongs to the Special Issue Advances in Vegetation Structure Modelling to Support the Sustainable Development Goals Acquisition through Forest Management)

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19 pages, 23245 KiB

Open AccessArticle

A First Assessment of Canopy Cover Loss in Germany’s Forests after the 2018–2020 Drought Years

by Frank Thonfeld, Ursula Gessner, Stefanie Holzwarth, Jennifer Kriese, Emmanuel da Ponte, Juliane Huth and Claudia Kuenzer

Remote Sens. 2022, 14(3), 562; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030562 - 25 Jan 2022

Cited by 38 | Viewed by 6258

Abstract

Central Europe was hit by several unusually strong periods of drought and heat between 2018 and 2020. These droughts affected forest ecosystems. Cascading effects with bark beetle infestations in spruce stands were fatal to vast forest areas in Germany. We present the first assessment of canopy cover loss in Germany for the period of January 2018–April 2021. Our approach makes use of dense Sentinel-2 and Landsat-8 time-series data. We computed the disturbance index (DI) from the tasseled cap components brightness, greenness, and wetness. Using quantiles, we generated monthly DI composites and calculated anomalies in a reference period (2017). From the resulting map, we calculated the canopy cover loss statistics for administrative entities. Our results show a canopy cover loss of 501,000 ha for Germany, with large regional differences. The losses were largest in central Germany and reached up to two-thirds of coniferous forest loss in some districts. Our map has high spatial (10 m) and temporal (monthly) resolution and can be updated at any time. Full article

(This article belongs to the Special Issue Advances in Vegetation Structure Modelling to Support the Sustainable Development Goals Acquisition through Forest Management)

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Other

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12 pages, 8452 KiB

Open AccessTechnical Note

Assessing the Efficacy of Phenological Spectral Differences to Detect Invasive Alien Acacia dealbata Using Sentinel-2 Data in Southern Europe

by Dario Domingo, Fernando Pérez-Rodríguez, Esteban Gómez-García and Francisco Rodríguez-Puerta

Remote Sens. 2023, 15(3), 722; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15030722 - 26 Jan 2023

Cited by 3 | Viewed by 1817

Abstract

Invasive alien plants are transforming the landscapes, threatening the most vulnerable elements of local biodiversity across the globe. The monitoring of invasive species is paramount for minimizing the impact on biodiversity. In this study, we aim to discriminate and identify the spatial extent of Acacia dealbata Link from other species using RGB-NIR Sentinel-2 data based on phenological spectral peak differences. Time series were processed using the Earth Engine platform and random forest importance was used to select the most suitable Sentinel-2 derived metrics. Thereafter, a random forest machine learning algorithm was trained to discriminate between A. dealbata and native species. A flowering period was detected in March and metrics based on the spectral difference between blooming and the pre flowering (January) or post flowering (May) months were highly suitable for A. dealbata discrimination. The best-fitted classification model shows an overall accuracy of 94%, including six Sentinel-2 derived metrics. We find that 55% of A. dealbata presences were widely widespread in patches replacing Pinus pinaster Ait. stands. This invasive alien species also creates continuous monospecific stands representing 33% of the presences. This approach demonstrates its value for detecting and mapping A. dealbata based on RGB-NIR bands and phenological peak differences between blooming and pre or post flowering months providing suitable information for an early detection of invasive species to improve sustainable forest management. Full article

(This article belongs to the Special Issue Advances in Vegetation Structure Modelling to Support the Sustainable Development Goals Acquisition through Forest Management)

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