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Forests
Special Issues
Remote Sensing Approaches to Mapping and Monitoring Forest Vegetation Conditions
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Remote Sensing Approaches to Mapping and Monitoring Forest Vegetation Conditions

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 May 2023) | Viewed by 14648

Special Issue Editors

Prof. Dr. Lucas Prado Osco

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Guest Editor
Post-Graduation Program of Environmental and Regional Development, Post-Graduate Program of Agronomy, University of Western São Paulo (UNOESTE), Presidente Prudente, SP, Brazil
Interests: remote sensing; image processing; geoprocessing; machine learning; deep learning; data analysis; spectroscopy; vegetation analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. José Marcato Junior

E-Mail Website
Guest Editor
Faculty of Engineering, Architecture and Urbanism and Geography, Federal University of Mato Grosso do Sul, Cidade Universitária, Av. Costa e Silva–Pioneiros, Campo Grande 79070-900, MS, Brazil
Interests: remote sensing; deep learning; photogrammetry
Prof. Dr. Wesley Nunes Gonçalves

E-Mail Website
Guest Editor
Faculty of Engineering, Architecture and Urbanism and Geography, Federal University of Mato Grosso do Sul, Cidade Universitária, Av. Costa e Silva–Pioneiros, Campo Grande 79070-900, MS, Brazil
Interests: computer vision; pattern recognition; machine learning; deep learning
Prof. Dr. Ana Paula Marques Ramos

E-Mail Website
Guest Editor
Post-Graduate Program of Environment and Regional Development, University of Western São Paulo, Rodovia Raposo Tavares, Km 572–Limoeiro, Presidente Prudente 19067-175, SP, Brazil
Interests: remote sensing; cartography; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Forest mapping is important for protective, conservative, and sustainable explorative practices of the environment. In the past decade, studies focusing on remote sensing applications for forest monitoring increased with the availability of technological and computational methods advances. In this sense, state-of-the-art methods are being proposed to deal with a variety of tasks in forest evaluation. To promote an interdisciplinary approach to the subject, we invite all studies focused on remote sensing approaches to mapping, monitoring, detecting or classifying forest vegetation conditions and characteristics at different scale processes, to contribute to this Special Issue. Remote sensing and forest mapping offer a wide range of applications, and different experiments, models, methods, and analyses are welcome. We particularly encourage studies that incorporate state-of-the-art methods based on machine and deep learning advances that result in a mapping approach. The analysis may vary in level of detail, sources of information, forest definition, and target groups.

Potential topics include, but are not limited to:

  • Vegetation monitoring and classification;
  • Segmentation or object detection;
  • Machine-learning or deep learning;
  • Species identification;
  • UAV, airborne, or satellite data;
  • RGB, multispectral, or hyperspectral imagery;
  • LiDAR, optical, infrared and radar;
  • Wildfire or deforestation practices mapping;
  • Urban forests;
  • Agricultural forests;
  • Submerged or underwater forests;
  • Carbon storage;
  • Dimensional analysis;
  • Local, regional, and global mapping.

Prof. Dr. Lucas Prado Osco
Prof. Dr. José Marcato Junior
Prof. Dr. Wesley Nunes Gonçalves
Prof. Dr. Ana Paula Marques Ramos
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 2600 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

  • monitoring
  • analysis
  • artificial intelligence
  • optical imagery
  • radar

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Published Papers (5 papers)

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Research

26 pages, 18811 KiB  
Article
MARC-Net: Terrain Classification in Parallel Network Architectures Containing Multiple Attention Mechanisms and Multi-Scale Residual Cascades
by Xiangsuo Fan, Xuyang Li, Chuan Yan, Jinlong Fan, Ling Yu, Nayi Wang and Lin Chen
Forests 2023, 14(5), 1060; https://doi.org/10.3390/f14051060 - 22 May 2023
Cited by 5 | Viewed by 1559
Abstract
To address the problem that traditional deep learning algorithms cannot fully utilize the correlation properties between spectral sequence information and the feature differences between different spectra, this paper proposes a parallel network architecture land-use classification based on a combined multi-head attention mechanism and [...] Read more.
To address the problem that traditional deep learning algorithms cannot fully utilize the correlation properties between spectral sequence information and the feature differences between different spectra, this paper proposes a parallel network architecture land-use classification based on a combined multi-head attention mechanism and multiscale residual cascade called MARC-Net. This parallel framework is firstly implemented by deeply mining the features generated by grouped spectral embedding for information among spectral features by adding a multi-head attention mechanism, which allows semantic features to have expressions from more subspaces while fully considering all spatial location interrelationships. Secondly, a multiscale residual cascade CNN (convolutional neural network) is designed to fully utilize the fused feature information at different scales, thereby improving the network’s ability to represent different levels of information. Lastly, the features obtained by the multi-head attention mechanism are fused with those obtained by the CNN, and the merged resultant features are downgraded through the fully connected layer to obtain the classification results and achieve pixel-level multispectral image classification. The findings show that the algorithm proposed in this paper has an aggregate precision of 97.22%, compared to that of the Vision Transformer (ViT) with 95.08%; its performance on the Sentinel-2 dataset shows a huge improvement. Moreover, this article mainly focuses on the change rate of forest land in the study area. The Forest land area was 125.1143 km2 in 2017, 105.6089 km2 in 2019, and 76.3699 km2 in 2021, with an increase of 15.59%, an decrease of 0.97%, and increase of 14.76% in 2017–2019, 2019–2021 and 2017–2021, respectively. Full article
(This article belongs to the Special Issue Remote Sensing Approaches to Mapping and Monitoring Forest Vegetation Conditions)
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32 pages, 10275 KiB  
Article
Tree Species Classification in a Complex Brazilian Tropical Forest Using Hyperspectral and LiDAR Data
by Rorai Pereira Martins-Neto, Antonio Maria Garcia Tommaselli, Nilton Nobuhiro Imai, Eija Honkavaara, Milto Miltiadou, Erika Akemi Saito Moriya and Hassan Camil David
Forests 2023, 14(5), 945; https://doi.org/10.3390/f14050945 - 4 May 2023
Cited by 6 | Viewed by 5215
Abstract
This study experiments with different combinations of UAV hyperspectral data and LiDAR metrics for classifying eight tree species found in a Brazilian Atlantic Forest remnant, the most degraded Brazilian biome with high fragmentation but with huge structural complexity. The selection of the species [...] Read more.
This study experiments with different combinations of UAV hyperspectral data and LiDAR metrics for classifying eight tree species found in a Brazilian Atlantic Forest remnant, the most degraded Brazilian biome with high fragmentation but with huge structural complexity. The selection of the species was done based on the number of tree samples, which exist in the plot data and in the fact the UAV imagery does not acquire information below the forest canopy. Due to the complexity of the forest, only species that exist in the upper canopy of the remnant were included in the classification. A combination of hyperspectral UAV images and LiDAR point clouds were in the experiment. The hyperspectral images were photogrammetric and radiometric processed to obtain orthomosaics with reflectance factor values. Raw spectra were extracted from the trees, and vegetation indices (VIs) were calculated. Regarding the LiDAR data, both the point cloud—referred to as Peak Returns (PR)—and the full-waveform (FWF) LiDAR were included in this study. The point clouds were processed to normalize the intensities and heights, and different metrics for each data type (PR and FWF) were extracted. Segmentation was preformed semi-automatically using the superpixel algorithm, followed with manual correction to ensure precise tree crown delineation before tree species classification. Thirteen different classification scenarios were tested. The scenarios included spectral features and LiDAR metrics either combined or not. The best result was obtained with all features transformed with principal component analysis with an accuracy of 76%, which did not differ significantly from the scenarios using the raw spectra or VIs with PR or FWF LiDAR metrics. The combination of spectral data with geometric information from LiDAR improved the classification of tree species in a complex tropical forest, and these results can serve to inform management and conservation practices of these forest remnants. Full article
(This article belongs to the Special Issue Remote Sensing Approaches to Mapping and Monitoring Forest Vegetation Conditions)
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Graphical abstract

19 pages, 8602 KiB  
Article
Response Mechanism of Leaf Area Index and Main Nutrient Content in Mangrove Supported by Hyperspectral Data
by Xiaohua Chen, Yuechao Yang, Donghui Zhang, Xusheng Li, Yu Gao, Lifu Zhang, Daming Wang, Jianhua Wang, Jin Wang and Jin Huang
Forests 2023, 14(4), 754; https://doi.org/10.3390/f14040754 - 6 Apr 2023
Cited by 3 | Viewed by 1632
Abstract
Mangrove is the key vegetation in the transitional zone between land and sea, and its health assessment can indicate the deep-level ecological information. The LAI and six key nutrients of mangrove were selected as quantitative evaluation indicators, and the decisive evaluation method of [...] Read more.
Mangrove is the key vegetation in the transitional zone between land and sea, and its health assessment can indicate the deep-level ecological information. The LAI and six key nutrients of mangrove were selected as quantitative evaluation indicators, and the decisive evaluation method of mangrove growth was expected. The mangrove reserve of Dongzhai Port National Nature Reserve in Hainan Province, China, was selected as the study area, with an area of 17.71 km2. The study area was divided into adjacent urban areas, aquaculture areas, and agricultural production areas, and key indicators are extracted from satellite hyperspectral data. The extraction process includes spectral data preprocessing, spectral transformation, spectral combination, spectral modeling, and precision inspection. The spatial distribution of LAI and six key nutrient components of mangrove in the study area were obtained. LAI and Chla need to calculate the index after high-order differentiation of the spectrum; MSTR and Chlb need to calculate the envelope after the second-order differential of the spectrum; TN and TP are directly changed by original or exponential spectrum; the spectral transformation method adopted by TK was homogenization after first-order differential. The results of the strength of nutrient content along the three regions show that there was no significant difference in the retrieval index of mangroves in the three regions, and the overall health level of mangroves was consistent. Chla was the key identification component of mangrove growth and health. The contents of nutrient elements with correlation coefficient exceeding 0.80 include MSTR and TK (0.98), Chla and TP (0.96), Chla and TK (0.87), MSTR and Chla (0.86), MSTR and TK (0.83), and MSTR and TP (0.81). The study quantifies the relationship between different LAI and nutrient content of mangrove leaves from the perspectives of water, leaf biology, and chemical elements, which improved our understanding of the relationship between key components during mangrove growth for the first time. Full article
(This article belongs to the Special Issue Remote Sensing Approaches to Mapping and Monitoring Forest Vegetation Conditions)
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14 pages, 5210 KiB  
Article
Monitoring Illegal Logging Using Google Earth Engine in Sulawesi Selatan Tropical Forest, Indonesia
by A. Mujetahid, Munajat Nursaputra and Andang Suryana Soma
Forests 2023, 14(3), 652; https://doi.org/10.3390/f14030652 - 22 Mar 2023
Cited by 4 | Viewed by 3314
Abstract
Forest destruction has been found to be the cause of natural disasters in the form of floods, landslides in the rainy season, droughts in the dry season, climate change, and global warming. The high rate of forest destruction is caused by various factors, [...] Read more.
Forest destruction has been found to be the cause of natural disasters in the form of floods, landslides in the rainy season, droughts in the dry season, climate change, and global warming. The high rate of forest destruction is caused by various factors, including weak law enforcement efforts against forestry crimes, such as illegal logging events. However, in Indonesia, illegal logging is only discovered when the perpetrator has distributed the wood products. The lack of monitoring of the overall condition of the forest has an impact on the current high level of forest destruction. Through this research, the problems related to environmental damage due to illegal logging will be described through remote sensing technology, which is currently mainly developed on the basis of artificial intelligence and machine learning, namely Google Earth Engine (GEE). Monitoring of illegal logging events will be analysed using Sentinel 1 and 2 data. Obtaining satellite imagery with relatively small cloud cover for tropical regions, such as Indonesia, is remarkably difficult. This difficulty is due to the presence of a radar sensor on Sentinel 1 images that can penetrate clouds, allowing for observation of the forest condition even in the presence of clouds. Using the random forest classification algorithm of the GEE platform, data on forest conditions in 2021 were obtained, covering an area of 2,843,938.87 ha or 63% of the total area of Sulawesi Selatan Province. An analysis using a map of the function of forest areas revealed that of the current forest area, 38.46% was non-forest estates and 61.54% was forest areas. The continued identification of illegal logging events also found 1971 spots of forest change events in the vulnerable time of the first period (January–April) with the second period (April–August), and 1680 spots of forest change events in the vulnerable period of the second period (April–August) with the third period (September–December), revealing a total incident area of 7599.28 ha. Full article
(This article belongs to the Special Issue Remote Sensing Approaches to Mapping and Monitoring Forest Vegetation Conditions)
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18 pages, 16988 KiB  
Article
Forest Land Resource Information Acquisition with Sentinel-2 Image Utilizing Support Vector Machine, K-Nearest Neighbor, Random Forest, Decision Trees and Multi-Layer Perceptron
by Chen Zhang, Yang Liu and Niu Tie
Forests 2023, 14(2), 254; https://doi.org/10.3390/f14020254 - 29 Jan 2023
Cited by 10 | Viewed by 2065
Abstract
Forestry work involves scientific management and the effective utilization of forest land resources, and finding economical, efficient and accurate acquisition methods for forest land resource information. In previous land-use classification research, machine learning algorithms have achieved good results, and Sentinel public data have [...] Read more.
Forestry work involves scientific management and the effective utilization of forest land resources, and finding economical, efficient and accurate acquisition methods for forest land resource information. In previous land-use classification research, machine learning algorithms have achieved good results, and Sentinel public data have been used in various remote sensing applications. However, there is a paucity of research using these data to evaluate the performance of machine learning algorithms in the extracting of complex forest land resource information. Using the Sentinel-2 satellite multispectral image data, the spectral reflectance, vegetation index characteristics and image texture characteristics of different forest land resources in the study area were calculated and compared. Then, based on three groups of features, the performances of the Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), decision trees (DT) and Multi-layer Perceptron (MLP) were examined and compared to identify and classify forest land resource types. The research indicates the following: (1) The SVM algorithm achieved the highest OA (95.8%). The average accuracy of the SVM algorithm was much higher than other algorithms (SVM 88.3%, KNN 87.5, RF 85.3%, MLP 85.00% and DT 77.5%). (2) The classification accuracies of each algorithm for coniferous forests were relatively high, and the recognition accuracy was above 95%, whereas the classification accuracies of the other categories varied greatly. (3) Adding texture features can improve the accuracy of the five algorithms. This study reports new references for the qualitative methods of forest land resource distribution. It has also produced more efficient and accurate acquisitions of forest land resource information, scientific management and effective use of forest land resources. Full article
(This article belongs to the Special Issue Remote Sensing Approaches to Mapping and Monitoring Forest Vegetation Conditions)
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