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Remote Sensing Applications for Forest Ecosystem Monitoring and Spatial Modeling

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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: 24 August 2024 | Viewed by 14560

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Special Issue Editors

Dr. Jan Komarek

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Guest Editor

Department of Spatial Sciences, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, 165 00 Prague, Czech Republic
Interests: multispectral remote sensing; UAV data analysis; vegetation inventory; landscape modelling

Dr. Marlena Kycko

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Guest Editor

Chair of Geomatics and Information Systems, Department of Geoinformatics, Cartography and Remote Sensing, Faculty of Geography and Regional Studies, University of Warsaw, 00-927 Warsaw, Poland
Interests: vegetation monitoring; vegetation condition; biophysical remote sensing; hyperspectral and multispectral remote sensing; geostatistics; image analysis; classification; algorithms

Prof. Dr. Iñigo Molina

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Guest Editor

Department of Topographic and Cartographic Engineering, Universidad Politécnica de Madrid, Madrid, Spain
Interests: soil moisture content (SMC); global navigation satellite systems reflectometry (GNSS-R); active-passive sensors; earth-science applications
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Special Issue Information

Dear Colleagues,

Forests, covering almost a third of the terrestrial land cover surface, represent one of the most sophisticated ecosystems. They provide countless ecosystem services, potentially mitigating the ongoing climate change. However, those services suffer from the increasing anthropogenic pressure and forest disturbances. To properly evaluate the effects, scientists worldwide are working to improve their abilities to monitor forest ecosystems and their change. Outside forests, networks of small landscape elements (groves, hedgerows, tree avenues, agroforestry, urban greenery, etc.) are not only of high importance for biodiversity conservation and restoration but also contribute to the quality of our cultural landscapes.

Remotely sensed data may be acquired over various spatial, spectral, and temporal resolutions for numerous purposes. Satellite imagery is traditionally used, e.g., for land cover change, mapping landscape dynamics, detection and monitoring of disturbances, deriving vegetation parameters and structure, or modeling (micro)climate change and effects. Imagery may be fused with lidar or radar data to provide a 3D forest structure. The acquisition of such a 3D structure has become even easier and more accessible with the improved capabilities and availability of unmanned aerial systems.

This Special Issue aims to collect studies covering different uses of different sensors and platforms in forest and landscape sciences. Multi-source data fusion and integration (e.g., multispectral, hyperspectral, thermal, microwave) and multi-scale and multi-temporal approaches, among other issues, are welcome. Studies focusing on the use of consumer-grade and low-cost solutions are also welcome.

Articles may address, but are not limited to, the following topics:

Tree and vegetation inventory;
Vegetation structural characteristics;
Land cover and landscape change;
Biotic and abiotic disturbances;
Phenological vegetation traits and trends;
(Micro)climate variable derivation;
Surface and terrain analysis;
Long-term monitoring.

Dr. Jan Komarek
Dr. Marlena Kycko
Prof. Dr. Iñigo Molina
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

forest inventory
landscape modelling
feature and pattern detection
long-term monitoring
image analysis
point cloud analysis
data fusion

Published Papers (7 papers)

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Research

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16 pages, 2113 KiB

Open AccessArticle

Assessing Forest Species Diversity in Ghana’s Tropical Forest Using PlanetScope Data

by Elisha Njomaba, James Nana Ofori, Reginald Tang Guuroh, Ben Emunah Aikins, Raymond Kwame Nagbija and Peter Surový

Remote Sens. 2024, 16(3), 463; https://doi.org/10.3390/rs16030463 - 25 Jan 2024

Viewed by 1002

Abstract

This study utilized a remotely sensed dataset with a high spatial resolution of 3 m to predict species diversity in the Bobiri Forest Reserve (BFR), a moist semi-deciduous tropical forest in Ghana. We conducted a field campaign of tree species measurements to achieve this objective for species diversity estimation. Thirty-five field plots of 50 m × 20 m were established, and the most dominant tree species within the forest were identified. Other measurements, such as diameter at breast height (DBH ≥ 5 cm), tree height, and each plot’s GPS coordinates, were recorded. The following species diversity indices were estimated from the field measurements: Shannon–Wiener (H′), Simpson diversity index (D₂), species richness (S), and species evenness (J′). The PlanetScope surface reflectance data at 3 m spatial resolution was acquired and preprocessed for species diversity prediction. The spectral/pixel information of all bands, except the coastal band, was extracted for further processing. Vegetation indices (VIs) (NDVI—normalized difference vegetation index, EVI—enhanced vegetation index, SRI—simple ratio index, SAVI—soil adjusted vegetation index, and NDRE—normalized difference red edge index) were also calculated from the spectral bands and their pixel value extracted. A correlation analysis was then performed between the spectral bands and VIs with the species diversity index. The results showed that spectral bands 6 (red) and 2 (blue) significantly correlated with the two main species diversity indices (S and H′) due to their influence on vegetation properties, such as canopy biomass and leaf chlorophyll content. Furthermore, we conducted a stepwise regression analysis to investigate the most important spectral bands to consider when estimating species diversity from the PlanetScope satellite data. Like the correlation results, bands 6 (red) and 2 (blue) were the most important bands to be considered for predicting species diversity. The model equations from the stepwise regression were used to predict tree species diversity. Overall, the study’s findings emphasize the relevance of remotely sensed data in assessing the ecological condition of protected areas, a tool for decision-making in biodiversity conservation. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Forest Ecosystem Monitoring and Spatial Modeling)

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24 pages, 13285 KiB

Open AccessArticle

Study on the Regeneration Probability of Understory Coniferous Saplings in the Liangshui Nature Reserve Based on Four Modeling Techniques

by Haiping Zhao, Yuman Sun, Weiwei Jia, Fan Wang, Zipeng Zhao and Simin Wu

Remote Sens. 2023, 15(19), 4869; https://doi.org/10.3390/rs15194869 - 08 Oct 2023

Viewed by 916

Abstract

Forests are one of the most important natural resources for humans, and understanding the regeneration probability of undergrowth in forests is very important for future forest spatial structure and forest management. In addition, the regeneration of understory saplings is a key process in the restoration of forest ecosystems. By studying the probability of sapling regeneration in forests, we can understand the impact of different stand factors and environmental factors on sapling regeneration. This could help provide a scientific basis for the restoration and protection of forest ecosystems. The Liangshui Nature Reserve of Yichun City, Heilongjiang Province, is a coniferous and broadleaved mixed forest. In this study, we assess the regeneration probability of coniferous saplings (CRP) in natural forests in 665 temporary plots in the Liangshui Nature Reserve. Using Sentinel-1 and Sentinel-2 images provided by the European Space Agency, as well as digital elevation model (DEM) data, we calculated the vegetation index, microwave vegetation index (RVI S1), VV, VH, texture features, slope, and DEM and combined them with field survey data to construct a logistic regression (LR) model, geographically weighted logistic regression (GWLR) model, random forest (RF) model, and multilayer perceptron (MLP) model to predict and analyze the CRP value of each pixel in the study area. The accuracy of the models was evaluated with the average values of the area under the ROC curve (AUC), kappa coefficient (KAPPA), root mean square error (RMSE), and mean absolute error (MAE) verified by five-fold cross-validation. The results showed that the RF model had the highest accuracy. The variable factor with the greatest impact on CRP was the DEM. The construction of the GWLR model considered more spatial factors and had a lower residual Moran index value. The four models had higher CRP prediction results in the low-latitude and low-longitude regions of the study area, and in the high-latitude and high-longitude regions of the study area, most pixels had a CRP value of 0 (i.e., no coniferous sapling regeneration occurred). Full article

(This article belongs to the Special Issue Remote Sensing Applications for Forest Ecosystem Monitoring and Spatial Modeling)

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12 pages, 977 KiB

Open AccessCommunication

Analysing Pine Disease Spread Using Random Point Process by Remote Sensing of a Forest Stand

by Rostyslav Kosarevych, Izabela Jonek-Kowalska, Bohdan Rusyn, Anatoliy Sachenko and Oleksiy Lutsyk

Remote Sens. 2023, 15(16), 3941; https://doi.org/10.3390/rs15163941 - 09 Aug 2023

Viewed by 860

Abstract

The application of a process model to investigate pine tree infestation caused by bark beetles is discussed. The analysis of this disease was carried out using spatial and spatio−temporal models of random point patterns. Spatial point patterns were constructed for remote sensing images of pine trees damaged by the apical bark beetle. The method of random point processes was used for their analysis. A number of known models of point pattern processes with pairwise interaction were fitted to actual data. The best model to describe the real data was chosen using the Akaike information index. The residual K−function was used to check the fit of the model to the real data. According to values of the Akaike information criterion and the residual K−function, two models were found to correspond best to the investigated data. These are the generalized Geyer model of the point process of saturation and the pair interaction process with the piecewise constant potential of a pair of points. For the first time, a spatio−temporal model of the contagious process was used for analysis of tree damage. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Forest Ecosystem Monitoring and Spatial Modeling)

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26 pages, 9425 KiB

Open AccessArticle

Road-Side Individual Tree Segmentation from Urban MLS Point Clouds Using Metric Learning

by Pengcheng Wang, Yong Tang, Zefan Liao, Yao Yan, Lei Dai, Shan Liu and Tengping Jiang

Remote Sens. 2023, 15(8), 1992; https://doi.org/10.3390/rs15081992 - 10 Apr 2023

Cited by 7 | Viewed by 2330

Abstract

As one of the most important components of urban space, an outdated inventory of road-side trees may misguide managers in the assessment and upgrade of urban environments, potentially affecting urban road quality. Therefore, automatic and accurate instance segmentation of road-side trees from urban point clouds is an important task in urban ecology research. However, previous works show under- or over-segmentation effects for road-side trees due to overlapping, irregular shapes and incompleteness. In this paper, a deep learning framework that combines semantic and instance segmentation is proposed to extract single road-side trees from vehicle-mounted mobile laser scanning (MLS) point clouds. In the semantic segmentation stage, the ground points are filtered to reduce the processing time. Subsequently, a graph-based semantic segmentation network is developed to segment road-side tree points from the raw MLS point clouds. For the individual tree segmentation stage, a novel joint instance and semantic segmentation network is adopted to detect instance-level roadside trees. Two complex Chinese urban point cloud scenes are used to evaluate the individual urban tree segmentation performance of the proposed method. The proposed method accurately extract approximately 90% of the road-side trees and achieve better segmentation results than existing published methods in both two urban MLS point clouds. Living Vegetation Volume (LVV) calculation can benefit from individual tree segmentation. The proposed method provides a promising solution for ecological construction based on the LVV calculation of urban roads. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Forest Ecosystem Monitoring and Spatial Modeling)

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27 pages, 3280 KiB

Open AccessArticle

Towards Forest Condition Assessment: Evaluating Small-Footprint Full-Waveform Airborne Laser Scanning Data for Deriving Forest Structural and Compositional Metrics

by Matthew J. Sumnall, Ross A. Hill and Shelley A. Hinsley

Remote Sens. 2022, 14(20), 5081; https://doi.org/10.3390/rs14205081 - 11 Oct 2022

Viewed by 1646

Abstract

Spatial data on forest structure, composition, regeneration and deadwood are required for informed assessment of forest condition and subsequent management decisions. Here, we estimate 27 forest metrics from small-footprint full-waveform airborne laser scanning (ALS) data using a random forest (RF) and automated variable selection (Boruta) approach. Modelling was conducted using leaf-off (April) and leaf-on (July) ALS data, both separately and combined. Field data from semi-natural deciduous and managed conifer plantation forests were used to generate the RF models. Based on NRMSE and NBias, overall model accuracies were good, with only two of the best 27 models having an NRMSE > 30% and/or NBias > 15% (Standing deadwood decay class and Number of sapling species). With the exception of the Simpson index of diversity for native trees, both NRMSE and NBias varied by less than ±4.5% points between leaf-on only, leaf-off only and combined leaf-on/leaf-off models per forest metric. However, whilst model performance was similar between ALS datasets, model composition was often very dissimilar in terms of input variables. RF models using leaf-on data showed a dominance of height variables, whilst leaf-off models had a dominance of width variables, reiterating that leaf-on and leaf-off ALS datasets capture different aspects of the forest and that structure and composition across the full vertical profile are highly inter-connected and therefore can be predicted equally well in different ways. A subset of 17 forest metrics was subsequently used to assess favourable conservation status (FCS), as a measure of forest condition. The most accurate RF models relevant to the 17 FCS indicator metrics were used to predict each forest metric across the field site and thresholds defining favourable conditions were applied. Binomial logistic regression was implemented to evaluate predicative accuracy probability relative to the thresholds, which varied from 0.73–0.98 area under the curve (AUC), where 11 of 17 metrics were >0.8. This enabled an index of forest condition (FCS) based on structure, composition, regeneration and deadwood to be mapped across the field site with reasonable certainty. The FCS map closely and consistently corresponded to forest types and stand boundaries, indicating that ALS data offer a feasible approach for forest condition mapping and monitoring to advance forest ecological understanding and improve conservation efforts. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Forest Ecosystem Monitoring and Spatial Modeling)

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Review

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18 pages, 6527 KiB

Open AccessReview

Plugging the Gaps in the Global PhenoCam Monitoring of Forests—The Need for a PhenoCam Network across Indian Forests

by Karun Jose, Rajiv Kumar Chaturvedi, Chockalingam Jeganathan, Mukunda Dev Behera and Chandra Prakash Singh

Remote Sens. 2023, 15(24), 5642; https://doi.org/10.3390/rs15245642 - 06 Dec 2023

Viewed by 2032

Abstract

Our understanding of the impact of climate change on forests is constrained by a lack of long-term phenological monitoring. It is generally carried out via (1) ground observations, (2) satellite-based remote sensing, and (3) near-surface remote sensing (e.g., PhenoCams, unmanned aerial vehicles, etc.). Ground-based observations are limited by space, time, funds, and human observer bias. Satellite-based phenological monitoring does not carry these limitations; however, it is generally associated with larger uncertainties due to atmospheric noise, land cover mixing, and the modifiable area unit problem. In this context, near-surface remote sensing technologies, e.g., PhenoCam, emerge as a promising alternative complementing ground and satellite-based observations. Ground-based phenological observations generally record the following key parameters: leaves (bud stage, mature, abscission), flowers (bud stage, anthesis, abscission), and fruit (bud stage, maturation, and abscission). This review suggests that most of these nine parameters can be recorded using PhenoCam with >90% accuracy. Currently, Phenocameras are situated in the US, Europe, and East Asia, with a stark paucity over Africa, South America, Central, South-East, and South Asia. There is a need to expand PhenoCam monitoring in underrepresented regions, especially in the tropics, to better understand global forest dynamics as well as the impact of global change on forest ecosystems. Here, we spotlight India and discuss the need for a new PhenoCam network covering the diversity of Indian forests and its possible applications in forest management at a local level. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Forest Ecosystem Monitoring and Spatial Modeling)

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34 pages, 1678 KiB

Open AccessReview

A Survey of Computer Vision Techniques for Forest Characterization and Carbon Monitoring Tasks

by Svetlana Illarionova, Dmitrii Shadrin, Polina Tregubova, Vladimir Ignatiev, Albert Efimov, Ivan Oseledets and Evgeny Burnaev

Remote Sens. 2022, 14(22), 5861; https://doi.org/10.3390/rs14225861 - 19 Nov 2022

Cited by 12 | Viewed by 3763

Abstract

Estimation of terrestrial carbon balance is one of the key tasks in the understanding and prognosis of climate change impacts and the development of tools and policies according to carbon mitigation and adaptation strategies. Forest ecosystems are one of the major pools of carbon stocks affected by controversial processes influencing carbon stability. Therefore, monitoring forest ecosystems is a key to proper inventory management of resources and planning their sustainable use. In this survey, we discuss which computer vision techniques are applicable to the most important aspects of forest management actions, considering the wide availability of remote sensing (RS) data of different resolutions based both on satellite and unmanned aerial vehicle (UAV) observations. Our analysis applies to the most occurring tasks such as estimation of forest areas, tree species classification, and estimation of forest resources. Through the survey, we also provide a necessary technical background with a description of suitable data sources, algorithms’ descriptions, and corresponding metrics for their evaluation. The implementation of the provided techniques into routine workflows is a significant step toward the development of systems of continuous actualization of forest data, including real-time monitoring. It is crucial for diverse purposes on both local and global scales. Among the most important are the implementation of improved forest management strategies and actions, carbon offset projects, and enhancement of the prediction accuracy of system changes under different land-use and climate scenarios. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Forest Ecosystem Monitoring and Spatial Modeling)

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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Harmonized Landsat and Sentinel-2 data with Google Earth Engine: a study case with forest phenology
Authors: Elias Berra (1), Denise Fontana (2), Magda Smigaj (3), Feng Yin (4)
Affiliation: (1) Federal University of Paraná, (2) Federal University of Rio Grande do Sul, (3) Wageningen University, (4) University College London

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