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Operationalization of Remote Sensing Solutions for Sustainable 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 (30 November 2020) | Viewed by 66649

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Special Issue Editors

Prof. Dr. Gintautas Mozgeris

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Guest Editor

Agriculture Academy, Faculty of Forest Sciences and Ecology, Department of Forest Sciences, Vytautas Magnus University, Studentų Str. 11, LT-53361 Akademija, Kaunas Region, Lithuania
Interests: geomatics; forest management; environmental policy
Special Issues, Collections and Topics in MDPI journals

Dr. Ivan Balenović

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Guest Editor

Croatian Forest Research Institute, Division for Forest Management and Forestry Economics, Trnjanska cesta 35, HR-10000 Zagreb, Croatia
Interests: forest inventory; photogrammetry (airborne, unmaned aerial systems); LiDAR (airborne, unmaned, terrestrial, mobile, personnel hand-held systems)

Special Issue Information

A pre-requisite for sustainable management of natural resources is the availability of timely, cost-effective and comprehensive information of the condition and development trends of the specific resource. Remote sensing has always been an essential source of such information. Specifically, forestry has long benefited from these techniques: the literature on analytic algorithms for forest areas based on remotely sensing data is abundant. Nonetheless, the operational applicability of research results needs to be improved. Moreover, remote sensing researchers often seek purely academic objectives, lacking the support and quality guidance from practical forest management objectives. Thus, science-driven solutions for knowledge transfer between researchers and forest stakeholders/policy makers/end users are becoming increasingly important.

With this Special Issue, we aim at compiling research papers dealing both with remote sensing methodologies and the implementation of research results to facilitate sustainable forest management. The focus is placed on remotely sensed data, the development of algorithms for forest site characterization, wood characterization, biomass and CO₂ stocking, mapping forest conditions, ecosystem vulnerabilities, socioeconomic functions and conditions, as well as on operationalization of remote sensing for natural resource management through the integration of scientific research and its practical utilisation. Overall, we call out for contributions combining remote sensing and any of the targets of the 2030 strategic research and innovation agenda for the forest based sector. Review contributions are also suitable for the Special Issue.

This special issue is linked with H2020 project MySustainableForest, however, contributions from other researchers are very welcome.

Prof. Dr. Gintautas Mozgeris
Dr. Ivan Balenović
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.

Keywords

Earth observation
forestry
characterization of forests
operationalization of remote sensing algorithms
knowledge transfer

Published Papers (14 papers)

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Editorial

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5 pages, 187 KiB

Open AccessEditorial

Operationalization of Remote Sensing Solutions for Sustainable Forest Management

by Gintautas Mozgeris and Ivan Balenović

Remote Sens. 2021, 13(4), 572; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040572 - 05 Feb 2021

Cited by 2 | Viewed by 1966

Abstract

The pre-requisite for sustainable management of natural resources is the availability of timely, cost-effective, and comprehensive information on the status and development trends of the management object [...] Full article

(This article belongs to the Special Issue Operationalization of Remote Sensing Solutions for Sustainable Forest Management)

Research

Jump to: Editorial, Other

18 pages, 4457 KiB

Open AccessArticle

Random Forest Regression Model for Estimation of the Growing Stock Volumes in Georgia, USA, Using Dense Landsat Time Series and FIA Dataset

by Shingo Obata, Chris J. Cieszewski, Roger C. Lowe III and Pete Bettinger

Remote Sens. 2021, 13(2), 218; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13020218 - 10 Jan 2021

Cited by 19 | Viewed by 3825

Abstract

The forest volumes are essential as they are directly related to the economic and environmental values of the forests. Satellite-based forest volume estimation was first developed in the 1990s, and the accuracy of the estimation has been improved over time. One of the satellite-based forest volume estimation issues is that it tends to overestimate the large volume class and underestimate the small volume class. Free availability of the major satellite imagery and the development of cloud-based computational platforms facilitate an immense amount of satellite imagery in the estimation. In this paper, we set three objectives: (1) to examine whether the long Landsat time series contributes to the improvement of the estimation accuracy, (2) to explore the effectiveness of forest disturbance record and land cover data as ancillary spatial data on the accuracy of the estimation, and (3) to apply the bias correction method to reduce the bias of the estimation. We computed three Tasseled-cap components from the Landsat data for preparation of short (2014–2016) and long (1984–2016) time series. Each data entity was analyzed with harmonic regressions resulting in the coefficients and the fitted values recorded as pixel values in a multilayer raster database. Data included Forest Inventory and Analysis (FIA) unit field inventory measurements provided by the United States Department of Agriculture Forest Service and the National Land Cover Database and disturbance history data added as ancillary information. The totality of the available data was organized into seven distinct Random Forest (RF) models with different variables compared against each other to identify the ones with the most satisfactory performance. A bias correction method was then applied to all the RF models to examine the effectiveness of the method. Among the seven models, the worst one used the coefficients and fitted values of the short Landsat time series only, and the best one used coefficients and fitted values of both short and long Landsat time series. Using the Out-of-bag (OOB) score, the best model was found to be 34.4% better than the worst one. The model that used only the long time series data had almost the same OOB score as the best model. The results indicate that the use of the long Landsat time series improves model performance. Contrary to the previous research employing forest disturbance data as a feature variable had almost no effect on OOB. The bias correction method reduced the relative size of the bias in the estimates of the best model from 3.79% to −1.47%, the bottom 10% bias by 12.5 points, and the top 10% bias by 9.9 points. Depending on the types of forest, important feature variables were differed, reflecting the relationship between the time series remote sensing data we computed for this research and the forests’ phenological characteristics. The availability of Light Detection And Ranging (LiDAR) data and accessibility of the precise locations of the FIA data are likely to improve the model estimates further. Full article

(This article belongs to the Special Issue Operationalization of Remote Sensing Solutions for Sustainable Forest Management)

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26 pages, 12903 KiB

Open AccessArticle

Phenology Modelling and Forest Disturbance Mapping with Sentinel-2 Time Series in Austria

by Markus Löw and Tatjana Koukal

Remote Sens. 2020, 12(24), 4191; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12244191 - 21 Dec 2020

Cited by 26 | Viewed by 5002

Abstract

Worldwide, forests provide natural resources and ecosystem services. However, forest ecosystems are threatened by increasing forest disturbance dynamics, caused by direct human activities or by altering environmental conditions. It is decisive to reconstruct and trace the intra- to transannual dynamics of forest ecosystems. National to local forest authorities and other stakeholders request detailed area-wide maps that delineate forest disturbance dynamics at various spatial scales. We developed a time series analysis (TSA) framework that comprises data download, data management, image preprocessing and an advanced but flexible TSA. We use dense Sentinel-2 time series and a dynamic Savitzky–Golay-filtering approach to model robust but sensitive phenology courses. Deviations from the phenology models are used to derive detailed spatiotemporal information on forest disturbances. In a first case study, we apply the TSA to map forest disturbances directly or indirectly linked to recurring bark beetle infestation in Northern Austria. In addition to spatially detailed maps, zonal statistics on different spatial scales provide aggregated information on the extent of forest disturbances between 2018 and 2019. The outcomes are (a) area-wide consistent data of individual phenology models and deduced phenology metrics for Austrian forests and (b) operational forest disturbance maps, useful to investigate and monitor forest disturbances to facilitate sustainable forest management. Full article

(This article belongs to the Special Issue Operationalization of Remote Sensing Solutions for Sustainable Forest Management)

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18 pages, 4159 KiB

Open AccessArticle

A Comparison of Two Machine Learning Classification Methods for Remote Sensing Predictive Modeling of the Forest Fire in the North-Eastern Siberia

by Piotr Janiec and Sébastien Gadal

Remote Sens. 2020, 12(24), 4157; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12244157 - 18 Dec 2020

Cited by 27 | Viewed by 4346

Abstract

The problem of forest fires in Yakutia is not as well studied as in other countries. Two methods of machine learning classifications were implemented to determine the risk of fire: MaxENT and random forest. The initial materials to define fire risk factors were satellite images and their products of various spatial and spectral resolution (Landsat TM, Modis TERRA, GMTED2010, VIIRS), vector data (OSM), and bioclimatic variables (WORLDCLIM). The results of the research showed a strong human influence on the risk in this region, despite the low population density. Anthropogenic factors showed a high correlation with the occurrence of wildfires, more than climatic or topographical factors. Other factors affect the risk of fires at the macroscale and microscale, which should be considered when modeling. The random forest method showed better results in the macroscale, however, the maximum entropy model was better in the microscale. The exclusion of variables that do not show a high correlation, does not always improve the modeling results. The random forest presence prediction model is a more accurate method and significantly reduces the risk territory. The reverse is the method of maximum entropy, which is not as accurate and classifies very large areas as endangered. Further study of this topic requires a clearer and conceptually developed approach to the application of remote sensing data. Therefore, this work makes sense to lay the foundations of the future, which is a completely automated fire risk assessment application in the Republic of Sakha. The results can be used in fire prophylactics and planning fire prevention. In the future, to determine the risk well, it is necessary to combine the obtained maps with the seasonal risk determined using indices (for example, the Nesterov index 1949) and the periodic dynamics of forest fires, which Isaev and Utkin studied in 1963. Such actions can help to build an application, with which it will be possible to determine the risk of wildfire and the spread of fire during extreme events. Full article

(This article belongs to the Special Issue Operationalization of Remote Sensing Solutions for Sustainable Forest Management)

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18 pages, 7989 KiB

Open AccessArticle

Tree Species Classification in Mixed Deciduous Forests Using Very High Spatial Resolution Satellite Imagery and Machine Learning Methods

by Martina Deur, Mateo Gašparović and Ivan Balenović

Remote Sens. 2020, 12(23), 3926; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12233926 - 30 Nov 2020

Cited by 37 | Viewed by 7031

Abstract

Spatially explicit information on tree species composition is important for both the forest management and conservation sectors. In combination with machine learning algorithms, very high-resolution satellite imagery may provide an effective solution to reduce the need for labor-intensive and time-consuming field-based surveys. In this study, we evaluated the possibility of using multispectral WorldView-3 (WV-3) satellite imagery for the classification of three main tree species (Quercus robur L., Carpinus betulus L., and Alnus glutinosa (L.) Geartn.) in a lowland, mixed deciduous forest in central Croatia. The pixel-based supervised classification was performed using two machine learning algorithms: random forest (RF) and support vector machine (SVM). Additionally, the contribution of gray level cooccurrence matrix (GLCM) texture features from WV-3 imagery in tree species classification was evaluated. Principal component analysis confirmed GLCM variance to be the most significant texture feature. Of the 373 visually interpreted reference polygons, 237 were used as training polygons and 136 were used as validation polygons. The validation results show relatively high overall accuracy (85%) for tree species classification based solely on WV-3 spectral characteristics and the RF classification approach. As expected, an improvement in classification accuracy was achieved by a combination of spectral and textural features. With the additional use of GLCM variance, the overall accuracy improved by 10% and 7% for RF and SVM classification approaches, respectively. Full article

(This article belongs to the Special Issue Operationalization of Remote Sensing Solutions for Sustainable Forest Management)

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29 pages, 18620 KiB

Open AccessArticle

Mapping of the Canopy Openings in Mixed Beech–Fir Forest at Sentinel-2 Subpixel Level Using UAV and Machine Learning Approach

by Ivan Pilaš, Mateo Gašparović, Alan Novkinić and Damir Klobučar

Remote Sens. 2020, 12(23), 3925; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12233925 - 30 Nov 2020

Cited by 12 | Viewed by 3917

Abstract

The presented study demonstrates a bi-sensor approach suitable for rapid and precise up-to-date mapping of forest canopy gaps for the larger spatial extent. The approach makes use of Unmanned Aerial Vehicle (UAV) red, green and blue (RGB) images on smaller areas for highly precise forest canopy mask creation. Sentinel-2 was used as a scaling platform for transferring information from the UAV to a wider spatial extent. Various approaches to an improvement in the predictive performance were examined: (I) the highest R² of the single satellite index was 0.57, (II) the highest R² using multiple features obtained from the single-date, S-2 image was 0.624, and (III) the highest R² on the multitemporal set of S-2 images was 0.697. Satellite indices such as Atmospherically Resistant Vegetation Index (ARVI), Infrared Percentage Vegetation Index (IPVI), Normalized Difference Index (NDI45), Pigment-Specific Simple Ratio Index (PSSRa), Modified Chlorophyll Absorption Ratio Index (MCARI), Color Index (CI), Redness Index (RI), and Normalized Difference Turbidity Index (NDTI) were the dominant predictors in most of the Machine Learning (ML) algorithms. The more complex ML algorithms such as the Support Vector Machines (SVM), Random Forest (RF), Stochastic Gradient Boosting (GBM), Extreme Gradient Boosting (XGBoost), and Catboost that provided the best performance on the training set exhibited weaker generalization capabilities. Therefore, a simpler and more robust Elastic Net (ENET) algorithm was chosen for the final map creation. Full article

(This article belongs to the Special Issue Operationalization of Remote Sensing Solutions for Sustainable Forest Management)

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29 pages, 5022 KiB

Open AccessArticle

Determining Optimal Location for Mangrove Planting Using Remote Sensing and Climate Model Projection in Southeast Asia

by Luri Nurlaila Syahid, Anjar Dimara Sakti, Riantini Virtriana, Ketut Wikantika, Wiwin Windupranata, Satoshi Tsuyuki, Rezzy Eko Caraka and Rudhi Pribadi

Remote Sens. 2020, 12(22), 3734; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12223734 - 13 Nov 2020

Cited by 25 | Viewed by 5484

Abstract

The decreasing area of mangroves is an ongoing problem since, between 1980 and 2005, one-third of the world’s mangroves were lost. Rehabilitation and restoration strategies are required to address this situation. However, mangroves do not always respond well to these strategies and have high mortality due to several growth limiting parameters. This study developed a land suitability map for new mangrove plantations in different Southeast Asian countries for both current and future climates at a 250-m resolution. Hydrodynamic, geomorphological, climatic, and socio-economic parameters and three representative concentration pathway (RCP) scenarios (RCP 2.6, 4.5, and 8.5) for 2050 and 2070 with two global climate model datasets (the Centre National de Recherches Météorologiques Climate model version 5 [CNRM-CM5.1] and the Model for Interdisciplinary Research on Climate [MIROC5]) were used to predict suitable areas for mangrove planting. An analytical hierarchy process (AHP) was used to determine the level of importance for each parameter. To test the accuracy of the results, the mangrove land suitability analysis were further compared using different weights in every parameter. The sensitivity test using the Wilcoxon test was also carried out to test which variables had changed with the first weight and the AHP weight. The land suitability products from this study were compared with those from previous studies. The differences in land suitability for each country in Southeast Asia in 2050 and 2070 to analyze the differences in each RCP scenario and their effects on the mangrove land suitability were also assessed. Currently, there is 398,000 ha of potentially suitable land for mangrove planting in Southeast Asia, and this study shows that it will increase between now and 2070. Indonesia account for 67.34% of the total land area in the “very suitable” and “suitable” class categories. The RCP 8.5 scenario in 2070, with both the MIROC5 and CNRM-CM5.1 models, resulted in the largest area of a “very suitable” class category for mangrove planting. This study provides information for the migration of mangrove forests to the land, alleviating many drawbacks, especially for ecosystems. Full article

(This article belongs to the Special Issue Operationalization of Remote Sensing Solutions for Sustainable Forest Management)

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19 pages, 22070 KiB

Open AccessArticle

Monitoring Bark Beetle Forest Damage in Central Europe. A Remote Sensing Approach Validated with Field Data

by Angel Fernandez-Carrillo, Zdeněk Patočka, Lumír Dobrovolný, Antonio Franco-Nieto and Beatriz Revilla-Romero

Remote Sens. 2020, 12(21), 3634; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12213634 - 05 Nov 2020

Cited by 47 | Viewed by 7806

Abstract

Over the last decades, climate change has triggered an increase in the frequency of spruce bark beetle (Ips typographus L.) in Central Europe. More than 50% of forests in the Czech Republic are seriously threatened by this pest, leading to high ecological and economic losses. The exponential increase of bark beetle infestation hinders the implementation of costly field campaigns to prevent and mitigate its effects. Remote sensing may help to overcome such limitations as it provides frequent and spatially continuous data on vegetation condition. Using Sentinel-2 images as main input, two models have been developed to test the ability of this data source to map bark beetle damage and severity. All models were based on a change detection approach, and required the generation of previous forest mask and dominant species maps. The first damage mapping model was developed for 2019 and 2020, and it was based on bi-temporal regressions in spruce areas to estimate forest vitality and bark beetle damage. A second model was developed for 2020 considering all forest area, but excluding clear-cuts and completely dead areas, in order to map only changes in stands dominated by alive trees. The three products were validated with in situ data. All the maps showed high accuracies (acc > 0.80). Accuracy was higher than 0.95 and F1-score was higher than 0.88 for areas with high severity, with omission errors under 0.09 in all cases. This confirmed the ability of all the models to detect bark beetle attack at the last phases. Areas with no damage or low severity showed more complex results. The no damage category yielded greater commission errors and relative bias (CEs = 0.30–0.42, relB = 0.42–0.51). The similar results obtained for 2020 leaving out clear-cuts and dead trees proved that the proposed methods could be used to help forest managers fight bark beetle pests. These biotic damage products based on Sentinel-2 can be set up for any location to derive regular forest vitality maps and inform of early damage. Full article

(This article belongs to the Special Issue Operationalization of Remote Sensing Solutions for Sustainable Forest Management)

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20 pages, 3503 KiB

Open AccessArticle

The Use of Remotely Sensed Data and Polish NFI Plots for Prediction of Growing Stock Volume Using Different Predictive Methods

by Paweł Hawryło, Saverio Francini, Gherardo Chirici, Francesca Giannetti, Karolina Parkitna, Grzegorz Krok, Krzysztof Mitelsztedt, Marek Lisańczuk, Krzysztof Stereńczak, Mariusz Ciesielski, Piotr Wężyk and Jarosław Socha

Remote Sens. 2020, 12(20), 3331; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12203331 - 13 Oct 2020

Cited by 23 | Viewed by 3476

Abstract

Forest growing stock volume (GSV) is an important parameter in the context of forest resource management. National Forest Inventories (NFIs) are routinely used to estimate forest parameters, including GSV, for national or international reporting. Remotely sensed data are increasingly used as a source of auxiliary information for NFI data to improve the spatial precision of forest parameter estimates. In this study, we combine data from the NFI in Poland with satellite images of Landsat 7 and 3D point clouds collected with airborne laser scanning (ALS) technology to develop predictive models of GSV. We applied an area-based approach using 13,323 sample plots measured within the second cycle of the NFI in Poland (2010–2014) with poor positional accuracy from several to 15 m. Four different predictive approaches were evaluated: multiple linear regression, k-Nearest Neighbours, Random Forest and Deep Learning fully connected neural network. For each of these predictive methods, three sets of predictors were tested: ALS-derived, Landsat-derived and a combination of both. The developed models were validated at the stand level using field measurements from 360 reference forest stands. The best accuracy (RMSE% = 24.2%) and lowest systematic error (bias% = −2.2%) were obtained with a deep learning approach when both ALS- and Landsat-derived predictors were used. However, the differences between the evaluated predictive approaches were marginal when using the same set of predictor variables. Only a slight increase in model performance was observed when adding the Landsat-derived predictors to the ALS-derived ones. The obtained results showed that GSV can be predicted at the stand level with relatively low bias and reasonable accuracy for coniferous species, even using field sample plots with poor positional accuracy for model development. Our findings are especially important in the context of GSV prediction in areas where NFI data are available but the collection of accurate positions of field plots is not possible or justified because of economic reasons. Full article

(This article belongs to the Special Issue Operationalization of Remote Sensing Solutions for Sustainable Forest Management)

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17 pages, 3025 KiB

Open AccessArticle

The Importance of High Resolution Digital Elevation Models for Improved Hydrological Simulations of a Mediterranean Forested Catchment

by João Rocha, André Duarte, Margarida Silva, Sérgio Fabres, José Vasques, Beatriz Revilla-Romero and Ana Quintela

Remote Sens. 2020, 12(20), 3287; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12203287 - 10 Oct 2020

Cited by 31 | Viewed by 4504

Abstract

Eco-hydrological models can be used to support effective land management and planning of forest resources. These models require a Digital Elevation Model (DEM), in order to accurately represent the morphological surface and to simulate catchment responses. This is particularly relevant on low altimetry catchments, where a high resolution DEM can result in a more accurate representation of terrain morphology (e.g., slope, flow direction), and therefore a better prediction of hydrological responses. This work intended to use Soil and Water Assessment Tool (SWAT) to assess the influence of DEM resolutions (1 m, 10 m and 30 m) on the accuracy of catchment representations and hydrological responses on a low relief forest catchment with a dry and hot summer Mediterranean climate. The catchment responses were simulated using independent SWAT models built up using three DEMs. These resolutions resulted in marked differences regarding the total number of channels, their length as well as the hierarchy. Model performance was increasingly improved using fine resolutions DEM, revealing a bR² (0.87, 0.85 and 0.85), NSE (0.84, 0.67 and 0.60) and Pbias (−14.1, −27.0 and −38.7), respectively, for 1 m, 10 m and 30 m resolutions. This translates into a better timing of the flow, improved volume simulation and significantly less underestimation of the flow. Full article

(This article belongs to the Special Issue Operationalization of Remote Sensing Solutions for Sustainable Forest Management)

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17 pages, 3311 KiB

Open AccessArticle

Designing a Validation Protocol for Remote Sensing Based Operational Forest Masks Applications. Comparison of Products Across Europe

by Angel Fernandez-Carrillo, Antonio Franco-Nieto, Erika Pinto-Bañuls, Miguel Basarte-Mena and Beatriz Revilla-Romero

Remote Sens. 2020, 12(19), 3159; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12193159 - 26 Sep 2020

Cited by 7 | Viewed by 3810

Abstract

The spatial and temporal dynamics of the forest cover can be captured using remote sensing data. Forest masks are a valuable tool to monitor forest characteristics, such as biomass, deforestation, health condition and disturbances. This study was carried out under the umbrella of the EC H2020 MySustainableForest (MSF) project. A key achievement has been the development of supervised classification methods for delineating forest cover. The forest masks presented here are binary forest/non-forest classification maps obtained using Sentinel-2 data for 16 study areas across Europe with different forest types. Performance metrics can be selected to measure accuracy of forest mask. However, large-scale reference datasets are scarce and typically cannot be considered as ground truth. In this study, we implemented a stratified random sampling system and the generation of a reference dataset based on visual interpretation of satellite images. This dataset was used for validation of the forest masks, MSF and two other similar products: HRL by Copernicus and FNF by the DLR. MSF forest masks showed a good performance (OA_MSF = 96.3%; DC_MSF = 96.5), with high overall accuracy (88.7–99.5%) across all the areas, and omission and commission errors were low and balanced (OE_MSF = 2.4%; CE_MSF = 4.5%; relB_MSF = 2%), while the other products showed on average lower accuracies (OA_HRL = 89.2%; OA_FNF = 76%). However, for all three products, the Mediterranean areas were challenging to model, where the complexity of forest structure led to relatively high omission errors (OE_MSF = 9.5%; OE_HRL = 59.5%; OE_FNF = 71.4%). Comparing these results with the vision from external local stakeholders highlighted the need of establishing clear large-scale validation datasets and protocols for remote sensing-based forest products. Future research will be done to test the MSF mask in forest types not present in Europe and compare new outputs to available reference datasets. Full article

(This article belongs to the Special Issue Operationalization of Remote Sensing Solutions for Sustainable Forest Management)

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20 pages, 13221 KiB

Open AccessArticle

Detection of Longhorned Borer Attack and Assessment in Eucalyptus Plantations Using UAV Imagery

by André Duarte, Luis Acevedo-Muñoz, Catarina I. Gonçalves, Luís Mota, Alexandre Sarmento, Margarida Silva, Sérgio Fabres, Nuno Borralho and Carlos Valente

Remote Sens. 2020, 12(19), 3153; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12193153 - 25 Sep 2020

Cited by 26 | Viewed by 4189

Abstract

Eucalyptus Longhorned Borers (ELB) are some of the most destructive pests in regions with Mediterranean climate. Low rainfall and extended dry summers cause stress in eucalyptus trees and facilitate ELB infestation. Due to the difficulty of monitoring the stands by traditional methods, remote sensing arises as an invaluable tool. The main goal of this study was to demonstrate the accuracy of unmanned aerial vehicle (UAV) multispectral imagery for detection and quantification of ELB damages in eucalyptus stands. To detect spatial damage, Otsu thresholding analysis was conducted with five imagery-derived vegetation indices (VIs) and classification accuracy was assessed. Treetops were calculated using the local maxima filter of a sliding window algorithm. Subsequently, large-scale mean-shift segmentation was performed to extract the crowns, and these were classified with random forest (RF). Forest density maps were produced with data obtained from RF classification. The normalized difference vegetation index (NDVI) presented the highest overall accuracy at 98.2% and 0.96 Kappa value. Random forest classification resulted in 98.5% accuracy and 0.94 Kappa value. The Otsu thresholding and random forest classification can be used by forest managers to assess the infestation. The aggregation of data offered by forest density maps can be a simple tool for supporting pest management. Full article

(This article belongs to the Special Issue Operationalization of Remote Sensing Solutions for Sustainable Forest Management)

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29 pages, 9112 KiB

Open AccessArticle

Multi-Source Remote Sensing Data Product Analysis: Investigating Anthropogenic and Naturogenic Impacts on Mangroves in Southeast Asia

by Anjar Dimara Sakti, Adam Irwansyah Fauzi, Felia Niwan Wilwatikta, Yoki Sepwanto Rajagukguk, Sonny Adhitya Sudhana, Lissa Fajri Yayusman, Luri Nurlaila Syahid, Tanakorn Sritarapipat, Jeark A. Principe, Nguyen Thi Quynh Trang, Endah Sulistyawati, Inggita Utami, Candra Wirawan Arief and Ketut Wikantika

Remote Sens. 2020, 12(17), 2720; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12172720 - 22 Aug 2020

Cited by 27 | Viewed by 6744

Abstract

This study investigated the drivers of degradation in Southeast Asian mangroves through multi-source remote sensing data products. The degradation drivers that affect approximately half of this area are unidentified; therefore, naturogenic and anthropogenic impacts on these mangroves were studied. Various global land cover (GLC) products were harmonized and examined to identify major anthropogenic changes affecting mangrove habitats. To investigate the naturogenic factors, the impact of the water balance was evaluated using the Normalized Difference Vegetation Index (NDVI), and evapotranspiration and precipitation data. Vegetation indices’ response in deforested mangrove regions depends significantly on the type of drivers. A trend analysis and break point detection of percentage of tree cover (PTC), percentage of non-tree vegetation (PNTV), and percentage of non-vegetation (PNV) datasets can aid in measuring, estimating, and tracing the drivers of change. The assimilation of GLC products suggests that agriculture and fisheries are the predominant drivers of mangrove degradation. The relationship between water balance and degradation shows that naturogenic drivers have a wider impact than anthropogenic drivers, and degradation in particular regions is likely to be a result of the accumulation of various drivers. In large-scale studies, remote sensing data products could be integrated as a remarkably powerful instrument in assisting evidence-based policy making. Full article

(This article belongs to the Special Issue Operationalization of Remote Sensing Solutions for Sustainable Forest Management)

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14 pages, 3830 KiB

Open AccessLetter

Assessing the Applicability of Mobile Laser Scanning for Mapping Forest Roads in the Republic of Korea

by Hyeongkeun Kweon, Jung Il Seo and Joon-Woo Lee

Remote Sens. 2020, 12(9), 1502; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12091502 - 08 May 2020

Cited by 6 | Viewed by 2549

Abstract

Forest roads are an essential facility for sustainable forest management and protection. With advances in survey technology, such as Light Detection and Ranging, forest road maps with greater accuracy and resolution can be produced. This study produced a 3D map for establishment of a forest road inventory using a Mobile Laser Scanning (MLS) device mounted on a vehicle in four study forest roads in Korea, in order to review its precision, accuracy and efficiency based on comparisons with mapping using Total Station (TS) and Global Navigation Satellite System (GNSS). We counted the points that consist of the cloud data of the maps to determine the degree of precision density, and then compared this with 50 points at 20-m intervals on the centerlines bisecting the widths of the study forest roads. Then, we evaluated the relative positional accuracy of the MLS data based on three criteria: the total length of each forest road; the Root Mean Square Error (RMSE) obtained from coordinate values of the MLS and TS surveys compared to the GNSS survey; and the ratios of the centerlines extracted by the MLS and TS surveys overlaid to the buffer zone by the GNSS survey. Finally, we estimated the time and cost per unit length for producing the map to examine the efficiency of MLS mapping compared to the other two surveys. The results showed that the point cloud data acquired by the MLS survey on the study forest roads had very high precision and so is sufficient to produce a 3D forest road map with high-precision density and a low RMSE value. Although the equipment rental cost is somewhat high, the fact that information targeting on all spatial elements of forest roads can be obtained with a low cost of labor is a benefit when evaluating the efficiency of MLS survey and mapping. Our findings are expected to provide a quantitative assessment of both maintaining sustainable effectiveness and preventing potential environmental damage of forest roads. Full article

(This article belongs to the Special Issue Operationalization of Remote Sensing Solutions for Sustainable Forest Management)

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