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Special Issue "Mapping Forest Vegetation via Remote Sensing Tools"

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A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: closed (15 March 2023) | Viewed by 5980

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Special Issue Editors

Prof. Dr. Filiz Bektas Balcik

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Guest Editor

Geomatics Engineering Department, Istanbul Technical University, Maslak, Istanbul 34469, Turkey
Interests: remote sensing; land use land cover mapping; classification methods; vegetation mapping; change detection; land surface temperature analysis; air quality monitoring

Prof. Dr. Fusun Balik Sanli

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Guest Editor

Department of Geomatic Engineering, Yildiz Technical University, Istanbul 34210, Turkey
Interests: GIS; optical remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Fabiana Caló

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Guest Editor

Institute for Electromagnetic Sensing of the Environment (IREA), Italian National Research Council, 328 Diocleziano, 80124 Napoli, Italy
Interests: synthetic aperture radar; geophysical techniques; radar imaging; remote sensing by radar; geophysical image processing; vegetation; vegetation mapping; wildfires; deformation; geographic information systems

Dr. Antonio Pepe

E-Mail Website
Guest Editor

IREA-CNR, 80124 Napoli, Italy
Interests: geodesy; ground deformations; synthetic aperture radar (SAR); interferometry SAR (InSAR) techniques; phase unwrapping; multi-track; satellite constellations
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forests have an essential role in supporting the Earth's ecological balance and environmental health because they sustain the global carbon cycle, the quality of water resources, and recreational potential.

Recent advancements in a variety of remote sensing data availability, innovative image-processing methodologies, and cloud computing technologies have provided a significant opportunity to observe and monitor forest vegetation on different scales from local to global.

The Special Issue will cover the application of remote sensing data from multiple platforms. Original research papers are expected to use the recently developed techniques to process a wide variety of remote sensing data for forest vegetation mapping. Both research papers and innovative review papers are invited.

High-quality contributions emphasizing (but not limited to) the topics listed below are solicited for the Special Issue:

Mapping and monitoring forest vegetation;
Multispectral, hyperspectral, Synthetic Aperture Radar (SAR), InSAR and LiDAR applications;
Multi-sensor integration for environmental assessment;
Application of advanced image processing methodologies for mapping forest vegetation;
Application of remote sensing systems to derive spatio-temporal information on forest distribution, forest vegetation discrimination, forest vegetation conditions, and deforestation.

Prof. Dr. Filiz Bektas Balcik
Prof. Dr. Fusun Balik Sanli
Dr. Fabiana Caló
Dr. Antonio Pepe
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. Forests is an international peer-reviewed open access monthly 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 2000 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 vegetation mapping
advanced image processing
image classification
multispectral data
hyperspectral data
SAR
LİDAR

Published Papers (6 papers)

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Research

Open AccessArticle

Assessing Phenological Shifts of Deciduous Forests in Turkey under Climate Change: An Assessment for Fagus orientalis with Daily MODIS Data for 19 Years

and

Forests 2023, 14(2), 413; https://doi.org/10.3390/f14020413 - 17 Feb 2023

Viewed by 1252

Abstract

Understanding how natural ecosystems are and will be responding to climate change is one of the primary goals of ecological research. Plant phenology is accepted as one of the most sensitive bioindicators of climate change due to its strong interactions with climate dynamics, and a vast number of studies from all around the world present evidence considering phenological shifts as a response to climatic changes. Land surface phenology (LSP) is also a valuable tool in the absence of observational phenology data for monitoring the aforementioned shift responses. Our aim was to investigate the phenological shifts of Fagus orientalis forests in Turkey by means of daily MODIS surface reflectance data (MOD09GA) for the period between 2002 and 2020. The normalized difference vegetation index (NDVI) was calculated for the entire Turkey extent. This extent was then masked for F. orientalis. These “Fagus pixels” were then filtered by a minimum of 80% spatial and an annual 20% temporal coverage. A combination of two methods was applied to the time series for smoothing and reconstruction and the start of season (SOS), end of season, and length of season parameters were extracted. Trends in these parameters over the 19-year period were analyzed. The results were in concert with the commonly reported earlier SOS pattern, by a Sen’s slope of −0.8 days year⁻¹. Lastly, the relationships between SOS and mean, maximum and minimum temperature, growing degree days (GDD), and chilling hours (CH) were investigated. Results showed that the most significant correlations were found between the mean SOS trend and accumulated CH and accumulated GDD with a base temperature of 2 °C, both for the February–March interval. The immediate need for a phenological observation network in Turkey and its region is discussed. Full article

(This article belongs to the Special Issue Mapping Forest Vegetation via Remote Sensing Tools)

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Open AccessFeature PaperArticle

Tree Species Classification over Cloudy Mountainous Regions by Spatiotemporal Fusion and Ensemble Classifier

and

Forests 2023, 14(1), 107; https://doi.org/10.3390/f14010107 - 05 Jan 2023

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Abstract

Accurate mapping of tree species is critical for the sustainable development of the forestry industry. However, the lack of cloud-free optical images makes it challenging to map tree species accurately in cloudy mountainous regions. In order to improve tree species identification in this context, a classification method using spatiotemporal fusion and ensemble classifier is proposed. The applicability of three spatiotemporal fusion methods, i.e., the spatial and temporal adaptive reflectance fusion model (STARFM), the flexible spatiotemporal data fusion (FSDAF), and the spatial and temporal nonlocal filter-based fusion model (STNLFFM), in fusing MODIS and Landsat 8 images was investigated. The fusion results in Helong City show that the STNLFFM algorithm generated the best fused images. The correlation coefficients between the fusion images and actual Landsat images on May 28 and October 19 were 0.9746 and 0.9226, respectively, with an average of 0.9486. Dense Landsat-like time series at 8-day time intervals were generated using this method. This time series imagery and topography-derived features were used as predictor variables. Four machine learning methods, i.e., K-nearest neighbors (KNN), random forest (RF), artificial neural networks (ANNs), and light gradient boosting machine (LightGBM), were selected for tree species classification in Helong City, Jilin Province. An ensemble classifier combining these classifiers was constructed to further improve the accuracy. The ensemble classifier consistently achieved the highest accuracy in almost all classification scenarios, with a maximum overall accuracy improvement of approximately 3.4% compared to the best base classifier. Compared to only using a single temporal image, utilizing dense time series and the ensemble classifier can improve the classification accuracy by about 20%, and the overall accuracy reaches 84.32%. In conclusion, using spatiotemporal fusion and the ensemble classifier can significantly enhance tree species identification in cloudy mountainous areas with poor data availability. Full article

(This article belongs to the Special Issue Mapping Forest Vegetation via Remote Sensing Tools)

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Open AccessArticle

Assessment of Small-Extent Forest Fires in Semi-Arid Environment in Jordan Using Sentinel-2 and Landsat Sensors Data

and

Forests 2023, 14(1), 41; https://doi.org/10.3390/f14010041 - 26 Dec 2022

Viewed by 772

Abstract

The objective of this study was to evaluate the separability potential of Sentinel-2A (MultiSpectral Instrument, MSI) and Landsat (Operational Land Imager, OLI and Thermal Infrared Sensor, TIRS) derived indices for detecting small-extent (<25 ha) forest fires areas and severity degrees. Three remote sensing indices [differenced Normalized Burn Ratio (dNBR), differenced Normalized Different Vegetation Index (dNDVI), and differenced surface temperature (dTST)] were used at three forest fires sites located in Northern Jordan; Ajloun (total burned area 23 ha), Dibbeen (burned area 10.5), and Sakeb (burned area 15 ha). Compared to ground reference data, Sentinel-2 MSI was able to delimit the fire perimeter more precisely than Landsat-8. The accuracy of detecting burned area (area of coincidence) in Sentinel-2 was 7%–26% higher that Landsat-8 OLI across sites. In addition, Sentinel-2 reduced the omission area by 28%–43% and the commission area by 6%–38% compared to Landsat-8 sensors. Higher accuracy in Sentinel-2 was attributed to higher spatial resolution and lower mixed pixel problem across the perimeter of burned area (mixed pixels within the fire perimeter for Sentinel-2, 8.5%–13.5% vs. 31%–52% for Landsat OLI). In addition, dNBR had higher accuracy (higher coincidence values and less omission and commission) than dNDVI and dTST. In terms of fire severity degrees, dNBR (the best fire index candidate) derived from both satellites sensors were only capable of detecting the severe spots “severely-burned” with producer accuracy >70%. In fact, the dNBR-Sentinel-2/Landsat-8 overall accuracy and Kappa coefficient for classifying fire severity degree were less than 70% across the studied sites, except for Sentinel-dNBR in Dibbeen (72.5%). In conclusion, Sentinel-dNBR and Landsat promise to delimitate forest fire perimeters of small-scale (<25 ha) areas, but further remotely-sensed techniques are require (e.g., Landsat-Sentinel data fusion) to improve the fire severity-separability potential. Full article

(This article belongs to the Special Issue Mapping Forest Vegetation via Remote Sensing Tools)

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Open AccessArticle

Mapping Secondary Vegetation of a Region of Deforestation Hotspot in the Brazilian Amazon: Performance Analysis of C- and L-Band SAR Data Acquired in the Rainy Season

Bárbara Hass Kiyohara

and

Edson Eyji Sano

Forests 2022, 13(9), 1457; https://doi.org/10.3390/f13091457 - 10 Sep 2022

Viewed by 761

Abstract

The re-suppression of secondary vegetation (SV) in the Brazilian Amazon for agriculture or land speculation occurs mostly in the rainy season. The use of optical images to monitor such re-suppression during the rainy season is limited because of the persistent cloud cover. This study aimed to evaluate the potential of C- and L-band SAR data acquired in the rainy season to discriminate SV in an area of new hotspot of deforestation in the municipality of Colniza, northwestern of Mato Grosso State, Brazil. This is the first time that the potential of dual-frequency SAR data was analyzed to discriminate SV, with an emphasis on data acquired during the rainy season. The L-band ALOS/PALSAR-2 and the C-band Sentinel-1 data acquired in March 2018 were processed to obtain backscattering coefficients and nine textural attributes were derived from the gray level co-occurrence matrix method (GLCM). Then, we classified the images based on the non-parametric Random Forest (RF) and Support Vector Machine (SVM) algorithms. The use of SAR textural attributes improved the discrimination capability of different LULC classes found in the study area. The results showed the best performance of ALOS/PALSAR-2 data classified by the RF algorithm to discriminate the following representative land use and land cover classes of the study area: primary forest, secondary forest, shrubby pasture, clean pasture, and bare soil, with an overall accuracy and Kappa coefficient of 84% and 0.78, respectively. The RF outperformed the SVM classifier to discriminate these five LULC classes in 14% of overall accuracy for both ALOS-2 and Sentinel-1 data sets. This study also showed that the textural attributes derived from the GLCM method are highly sensitive to the moving window size to be applied to the GLCM method. The results of this study can assist the future development of an operation system based on dual-frequency SAR data to monitor re-suppression of SV in the Brazilian Amazon or in other tropical rainforests. Full article

(This article belongs to the Special Issue Mapping Forest Vegetation via Remote Sensing Tools)

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Open AccessArticle

Discrimination of Mangrove Stages Using Multitemporal Sentinel-1 C-Band Backscatter and Sentinel-2 Data—A Case Study in Samut Songkhram Province, Thailand

Kamonporn Upakankaew

Sarawut Ninsawat

Salvatore G. P. Virdis

and

Nophea Sasaki

Forests 2022, 13(9), 1433; https://doi.org/10.3390/f13091433 - 07 Sep 2022

Viewed by 876

Abstract

Discrimination of mangrove stage changes is useful for the conservation of this valuable natural resource. However, present-day optical satellite imagery is not fully reliable due to its high sensitivity to weather conditions and tidal variables. Here, we used the Vertical Transmit—Vertical Receive Polarization (VV) and Vertical Transmit—Horizontal Receive Polarization (VH) backscatter from the same and multiple-incidence angles from Sentinel-1 SAR C-band along with Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), Normalized Difference Red Edge (

{NDVI}_{RE}

) and Chlorophyll Index Green (

{CI}_{Green}

) from the optical satellite imageries from Sentinel-2 to discriminate between the changes in disturbance, recovery, and healthy mangrove stages in Samut Songkhram province, Thailand. We found the mean NDVI values to be 0.08 (±0.11), 0.19 (±0.09), and −0.53 (±0.16) for the three stages, respectively. We further found their correlation with VH backscatter from the multiple-incidence angles at about −17.98 (±2.34), −16.43 (±1.59), and −13.40 (±1.07), respectively. The VH backscatter from multiple-incidence angles was correlated with NDVI using Pearson’s correlation (𝑟² = 0.62). However, Pearson’s correlation of a single plot (ID2) of mangrove stage change from disturbance to recovery, and then on to the healthy mangrove stage, displayed a 𝑟² of 0.93 (p value is less than 0.0001, n = 34). This indicated that the multitemporal Sentinel-1 C-band backscatter and Sentinel-2 data could be used to discriminate mangrove stages, and that a reduced correlation to significant observations was the result of variations in both optical and SAR backscatter data. Full article

(This article belongs to the Special Issue Mapping Forest Vegetation via Remote Sensing Tools)

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Open AccessArticle

Brown Bear Food-Probability Models in West-European Russia: On the Way to the Real Resource Selection Function

Sergey S. Ogurtsov

Anatoliy A. Khapugin

Anatoliy S. Zheltukhin

Elena B. Fedoseeva

Alexander V. Antropov

María del Mar Delgado

and

Vincenzo Penteriani

Forests 2022, 13(8), 1247; https://doi.org/10.3390/f13081247 - 07 Aug 2022

Viewed by 1211

Abstract

Most habitat suitability models and resource selection functions (RSFs) use indirect variables and habitat surrogates. However, it is known that in order to adequately reflect the habitat requirements of a species, it is necessary to use proximal resource variables. Direct predictors should be used to construct a real RSF that reflects the real influence of main resources on species habitat use. In this work, we model the spatial distribution of the main food resources of brown bear Ursus arctos L. within the natural and human-modified landscapes of the Central Forest State Nature Reserve (CFNR) for further RSF construction. Food-probability models were built for Apiaceae spp. (Angelica sylvestris L., Aegopodium podagraria L., Chaerophyllum aromaticum L.), Populus tremula L., Vaccinium myrtillus L., V. microcarpum (Turcz. ex Rupr.) Schmalh., V. oxycoccos L., Corylus avellana L., Sorbus aucuparia L., Malus domestica Borkh., anthills, xylobiont insects, social wasps and Alces alces L. using the MaxEnt algorithm. For model evaluation, we used spatial block cross-validation and held apart fully independent data. The true skill statistic (TSS) estimates ranged from 0.34 to 0.95. Distribution of Apiaceae forbs was associated with areas having rich phytomass and moist conditions on southeastern slopes. Populus tremula preferred areas with phytomass abundance on elevated sites. Vaccinium myrtillus was confined to wet boreal spruce forests. V. microcarpum and V. oxycoccos were associated with raised bogs in depressions of the terrain. Corylus avellana and Sorbus aucuparia preferred mixed forests on elevated sites. Distribution of Malus domestica was associated with meadows with dry soils in places of abandoned cultural landscapes. Anthills were common on the dry soils of meadows, and the periphery of forest areas with high illumination and low percent cover of tree canopy. Moose preferred riverine flood meadows rich in herbaceous vegetation and sparse mixed forests in spring and early summer. The territory of the human-modified CFNR buffer zone was shown to contain a higher variety of food resources than the strictly protected CFNR core area. Full article

(This article belongs to the Special Issue Mapping Forest Vegetation via Remote Sensing Tools)

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