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Structure and Trend Monitoring of Forest Vegetation and Savanna Based on UAS Platform

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 21068

Special Issue Editors

LAPIG—Image Processing and GIS Laboratory, Institute of Socio-Environmental Studies, Samambaia Campus, Federal University of Goiás, Av. Esperança s/n, Goiânia 74690-900, GO, Brazil
Interests: environmental analysis with Remote Sensing and GIS technics; Ummanned Aerial Systems; geographic database and web map platforms
Special Issues, Collections and Topics in MDPI journals
Embrapa Cerrados, Rodovia BR 020 Km18, Brasília, DF 73310-970, Brazil
Interests: land use and land cover mapping; remote sensing in agriculture; remote sensing of environment
Federal University of Mato Grosso/FENF/LabSensoR, R. Quarenta e Nove, 2367 - Boa Esperança, Cuiabá - MT 78060-900, Brazil
Interests: Remotely Piloted Aircraft Systems (RPAS); wetlands and forest mapping; digital image processing; remote sensing of environment

Special Issue Information

Dear Colleagues,

Research on remote sensing techniques has undergone major changes in the past decade, in terms of data acquisition and applications. In the light of recent technological revolutions, unmanned aerial systems (UAS) represent an important part of these advances, especially in the monitoring of forest and savanna ecosystems around the Earth. Given their flexibility of spatial, temporal and spectral resolution, UAS are capable of differentiating tree species, vegetation structure, plant growth and productivity, biomass, and terrain features related to soil, geomorphology, and geology. In times of climate change, water resources shortage, and increasing levels of burning and deforestation, it is essential that new remote sensing technologies are used to monitor different aspects of the landscape and environment. In this special issue, emphasis will be given to forest and savanna ecosystems. In this scope, we would like to invite you to send submissions on the following topics:

  • Land use and land cover mapping.
  • Monitoring ecosystem services: soil, water and carbon stock.
  • Biomass and tree species identification.
  • Geomorphology and geology analyses.
  • Multispectral, hyperspectral and LiDAR applications.
  • Integrating long-term field and UAS remotely sensed data.
  • Fire risk and impacts on vegetation structure.
  • Monitoring of forest/savanna restoration structure parameters.
  • Multi-sensor integration for environmental and climate assessment.
  • UAS remote sensing data for planning and policy making.
  • Future trends and gaps in UAS remote sensing science.

Dr. Manuel Eduardo Ferreira
Dr.Edson Eyji Sano
Dr.Gustavo Manzon Nunes
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

  • Unmanned Aerial Vehicle
  • UAS applications in forest and savanna
  • Terrain analysis
  • Biomass and carbon stock
  • Canopy height and tree species
  • Advanced onboard UAS sensors

Published Papers (8 papers)

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Research

21 pages, 52577 KiB  
Article
Use of Remotely Piloted Aircraft System Multispectral Data to Evaluate the Effects of Prescribed Burnings on Three Macrohabitats of Pantanal, Brazil
by Harold E. Pineda Valles, Gustavo Manzon Nunes, Christian Niel Berlinck, Luiz Gustavo Gonçalves and Gabriel Henrique Pires de Mello Ribeiro
Remote Sens. 2023, 15(11), 2934; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15112934 - 04 Jun 2023
Viewed by 1507
Abstract
The controlled use of fires to reduce combustible materials in prescribed burning helps to prevent the occurrence of forest fires. In recent decades, these fires have mainly been caused by anthropogenic activities. The study area is located in the Pantanal biome. In 2020, [...] Read more.
The controlled use of fires to reduce combustible materials in prescribed burning helps to prevent the occurrence of forest fires. In recent decades, these fires have mainly been caused by anthropogenic activities. The study area is located in the Pantanal biome. In 2020, the greatest drought in 60 years happened in the Pantanal. The fire affected almost one third of the biome. The objective of this study is to evaluate the effect of prescribed burnings carried out in 2021 on three macrohabitats (M1: natural grassland flooded with a proliferation of Combretum spp., M2: natural grassland of seasonal swamps, and M3: natural grassland flooded with a proliferation of Vochysia divergens) inside the SESC Pantanal Private Natural Heritage Reserve. Multispectral and thermal data analyses were conducted with remotely piloted aircraft systems in 1 ha plots in three periods of the dry season with early, mid, and late burning. The land use and land cover classification indicate that the predominant vegetation type in these areas is seasonally flooded grassland, with percentages above 73%, except in zone three, which has a more diverse composition and structure, with the presence of arboreal specimens of V. divergem Pohl. The pattern of the thermal range showed differentiation pre- and post-burning. The burned area index indicated that fire was more efficient in the first two macrohabitats because they are natural grasslands, reducing the grass species in the burnings. Early and mid prescribed burnings are a good option to reduce the continuous accumulation of dry forest biomass fuel material and help to promote landscape heterogeneity. The use of multispectral sensor data with high spatial/spectral resolution can show the effects of fires, using highly detailed scales for technical decision making. Full article
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26 pages, 5920 KiB  
Article
Woody Plant Encroachment in a Seasonal Tropical Savanna: Lessons about Classifiers and Accuracy from UAV Images
by Lucas Silva Costa, Edson Eyji Sano, Manuel Eduardo Ferreira, Cássia Beatriz Rodrigues Munhoz, João Vítor Silva Costa, Leomar Rufino Alves Júnior, Thiago Roure Bandeira de Mello and Mercedes Maria da Cunha Bustamante
Remote Sens. 2023, 15(9), 2342; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15092342 - 28 Apr 2023
Cited by 1 | Viewed by 1788
Abstract
Woody plant encroachment in grassy ecosystems is a widely reported phenomenon associated with negative impacts on ecosystem functions. Most studies of this phenomenon have been carried out in arid and semi-arid grasslands. Therefore, studies in tropical regions, particularly savannas, which are composed of [...] Read more.
Woody plant encroachment in grassy ecosystems is a widely reported phenomenon associated with negative impacts on ecosystem functions. Most studies of this phenomenon have been carried out in arid and semi-arid grasslands. Therefore, studies in tropical regions, particularly savannas, which are composed of grassland and woodland mosaics, are needed. Our objective was to evaluate the accuracy of woody encroachment classification in the Brazilian Cerrado, a tropical savanna. We acquired dry and wet season unmanned aerial vehicle (UAV) images using RGB and multispectral cameras that were processed by the support vector machine (SVM), decision tree (DT), and random forest (RF) classifiers. We also compared two validation methods: the orthomosaic and in situ methods. We targeted two native woody species: Baccharis retusa and Trembleya parviflora. Identification of these two species was statistically (p < 0.05) most accurate in the wet season RGB images classified by the RF algorithm, with an overall accuracy (OA) of 92.7%. Relating to validation assessments, the in situ method was more susceptible to underfitting scenarios, especially using an RF classifier. The OA was higher in grassland than in woodland formations. Our results show that woody encroachment classification in a tropical savanna is possible using UAV images and field surveys and is suggested to be conducted during the wet season. It is challenging to classify UAV images in highly diverse ecosystems such as the Cerrado; therefore, whenever possible, researchers should use multiple accuracy assessment methods. In the case of using in situ accuracy assessment, we suggest a minimum of 40 training samples per class and to use multiple classifiers (e.g., RF and DT). Our findings contribute to the generation of tools that optimize time and cost for the monitoring and management of woody encroachment in tropical savannas. Full article
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21 pages, 3958 KiB  
Article
Satellite, UAV, and Geophysical Data to Identify Surface and Subsurface Hydrodynamics of Geographically Isolated Wetlands: Understanding an Undervalued Ecosystem at the Atlantic Forest-Cerrado Interface of Brazil
by Lucas Moreira Furlan, Manuel Eduardo Ferreira, César Augusto Moreira, Paulo Guilherme de Alencar, Matheus Felipe Stanfoca Casagrande and Vânia Rosolen
Remote Sens. 2023, 15(7), 1870; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15071870 - 31 Mar 2023
Cited by 1 | Viewed by 1474
Abstract
In two small and isolated wetlands located at the interface of the Atlantic Forest and Brazilian savanna (Cerrado) in São Paulo State, Brazil, we employed a pixel-based supervised classification approach using a combination of panchromatic and multispectral bands obtained from Landsat 2, 5, [...] Read more.
In two small and isolated wetlands located at the interface of the Atlantic Forest and Brazilian savanna (Cerrado) in São Paulo State, Brazil, we employed a pixel-based supervised classification approach using a combination of panchromatic and multispectral bands obtained from Landsat 2, 5, 7, and CBERS-04A satellites (ranging from 80 to 2 m/pixel). In addition, we acquired DJI Phantom 4 Pro UAV-RGB images in twelve different periods with a resolution of +5 cm/pixel. Furthermore, we utilized 2D and 3D Electrical Resistivity Tomography (ERT) to obtain data on the surroundings and center of the wetlands. Finally, we conducted a climatological data analysis. The results from the multisource data allowed us to classify the ecosystems as geographically isolated wetlands (GIWs), for which we documented a seasonal month-to-month (12 months) spatial variation of inundated area, vegetation pattern, soil water interaction, and a point of surface and deep-subsurface water interaction. These results are essential for high-accuracy characterization of small wetlands’ hydrodynamics and hydroperiods at the local scale. Our study contributes to optimizing GIWs understanding, monitoring, and reapplication of the methodology in other wetlands or small ecosystems. Full article
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35 pages, 4533 KiB  
Article
Mapping Areas Invaded by Pinus sp. from Geographic Object-Based Image Analysis (GEOBIA) Applied on RPAS (Drone) Color Images
by Vinicius Paiva Gonçalves, Eduardo Augusto Werneck Ribeiro and Nilton Nobuhiro Imai
Remote Sens. 2022, 14(12), 2805; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14122805 - 11 Jun 2022
Cited by 10 | Viewed by 2639
Abstract
Invasive alien species reduce biodiversity. In southern Brazil, the genus Pinus is considered invasive, and its dispersal by humans has resulted in this species reaching ecosystems that are more sensitive and less suitable for cultivation, as is the case for the restingas on [...] Read more.
Invasive alien species reduce biodiversity. In southern Brazil, the genus Pinus is considered invasive, and its dispersal by humans has resulted in this species reaching ecosystems that are more sensitive and less suitable for cultivation, as is the case for the restingas on Santa Catarina Island. Invasion control requires persistent efforts to identify and treat each new invasion case as a priority. In this study, areas invaded by Pinus sp. in restingas were mapped using images taken by a remotely piloted aircraft system (RPAS, or drone) to identify the invasion areas in great detail, enabling management to be planned for the most recently invaded areas, where management is simpler, more effective, and less costly. Geographic object-based image analysis (GEOBIA) was applied on images taken from a conventional RGB camera embedded in an RPAS, which resulted in a global accuracy of 89.56%, a mean kappa index of 0.86, and an F-score of 0.90 for Pinus sp. Processing was conducted with open-source software to reduce operational costs. Full article
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20 pages, 3364 KiB  
Article
Combining Different Transformations of Ground Hyperspectral Data with Unmanned Aerial Vehicle (UAV) Images for Anthocyanin Estimation in Tree Peony Leaves
by Lili Luo, Qinrui Chang, Yifan Gao, Danyao Jiang and Fenling Li
Remote Sens. 2022, 14(9), 2271; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14092271 - 08 May 2022
Cited by 10 | Viewed by 2533
Abstract
To explore rapid anthocyanin (Anth) detection technology based on remote sensing (RS) in tree peony leaves, we considered 30 species of tree peonies located in Shaanxi Province, China. We used an SVC HR~1024i portable ground object spectrometer and mini-unmanned aerial vehicle (UAV)-borne RS [...] Read more.
To explore rapid anthocyanin (Anth) detection technology based on remote sensing (RS) in tree peony leaves, we considered 30 species of tree peonies located in Shaanxi Province, China. We used an SVC HR~1024i portable ground object spectrometer and mini-unmanned aerial vehicle (UAV)-borne RS systems to obtain hyperspectral (HS) reflectance and images of canopy leaves. First, we performed principal component analysis (PCA), first-order differential (FD), and continuum removal (CR) transformations on the original ground-based spectra; commonly used spectral parameters were implemented to estimate Anth content using multiple stepwise regression (MSR), partial least squares (PLS), back-propagation neural network (BPNN), and random forest (RF) models. The spectral transformation highlighted the characteristics of spectral curves and improved the relationship between spectral reflectance and Anth, and the RF model based on the FD spectrum portrayed the best estimation accuracy (R2c = 0.91; R2v = 0.51). Then, the RGB (red-green-blue) gray vegetation index (VI) and the texture parameters were constructed using UAV images, and an Anth estimation model was constructed using UAV parameters. Finally, the UAV image was fused with the ground spectral data, and a multisource RS model of Anth estimation was constructed, based on PCA + UAV, FD + UAV, and CR + UAV, using MSR, PLS, BPNN, and RF methods. The RF model based on FD+UAV portrayed the best modeling and verification effect (R2c = 0.93; R2v = 0.76); compared with the FD-RF model, R2c increased only slightly, but R2v increased greatly from 0.51 to 0.76, indicating improved modeling and testing accuracy. The optimal spectral transformation for the Anth estimation of tree peony leaves was obtained, and a high-precision Anth multisource RS model was constructed. Our results can be used for the selection of ground-based HS transformation in future plant Anth estimation, and as a theoretical basis for plant growth monitoring based on ground and UAV multisource RS. Full article
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22 pages, 6759 KiB  
Article
Woody Plant Encroachment: Evaluating Methodologies for Semiarid Woody Species Classification from Drone Images
by Horia G. Olariu, Lonesome Malambo, Sorin C. Popescu, Clifton Virgil and Bradford P. Wilcox
Remote Sens. 2022, 14(7), 1665; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071665 - 30 Mar 2022
Cited by 9 | Viewed by 2707
Abstract
Globally, native semiarid grasslands and savannas have experienced a densification of woody plant species—leading to a multitude of environmental, economic, and cultural changes. These encroached areas are unique in that the diversity of tree species is small, but at the same time the [...] Read more.
Globally, native semiarid grasslands and savannas have experienced a densification of woody plant species—leading to a multitude of environmental, economic, and cultural changes. These encroached areas are unique in that the diversity of tree species is small, but at the same time the individual species possess diverse phenological responses. The overall goal of this study was to evaluate the ability of very high resolution drone imagery to accurately map species of woody plants encroaching on semiarid grasslands. For a site in the Edwards Plateau ecoregion of central Texas, we used affordable, very high resolution drone imagery to which we applied maximum likelihood (ML), support vector machine (SVM), random forest (RF), and VGG-19 convolutional neural network (CNN) algorithms in combination with pixel-based (with and without post-processing) and object-based (small and large) classification methods. Based on test sample data (n = 1000) the VGG-19 CNN model achieved the highest overall accuracy (96.9%). SVM came in second with an average classification accuracy of 91.2% across all methods, followed by RF (89.7%) and ML (86.8%). Overall, our findings show that RGB drone sensors are indeed capable of providing highly accurate classifications of woody plant species in semiarid landscapes—comparable to and even greater in some regards to those achieved by aerial and drone imagery using hyperspectral sensors in more diverse landscapes. Full article
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19 pages, 17943 KiB  
Article
Integrated Fire Management as a Renewing Agent of Native Vegetation and Inhibitor of Invasive Plants in Vereda Habitats: Diagnosis by Remotely Piloted Aircraft Systems
by Jéssika Cristina Nascente, Manuel Eduardo Ferreira and Gustavo Manzon Nunes
Remote Sens. 2022, 14(4), 1040; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14041040 - 21 Feb 2022
Cited by 2 | Viewed by 2854
Abstract
The Cerrado biome is being gradually reduced. Remote sensing has been widely used to investigate spatio-temporal changes in the landscape, which are frequently limited to mapping with orbital sensors, while the Remotely Piloted Aircraft System (RPAS) proved to be advantageous in terms of [...] Read more.
The Cerrado biome is being gradually reduced. Remote sensing has been widely used to investigate spatio-temporal changes in the landscape, which are frequently limited to mapping with orbital sensors, while the Remotely Piloted Aircraft System (RPAS) proved to be advantageous in terms of spatial resolution and the application of advanced digital processing techniques. In this study, we investigated a vereda (humid area) of a conservation unit in the state of Mato Grosso, Brazil. Object-Based Image Analysis (OBIA) was applied to images obtained by RPAS to distinguish the phytophysiognomies of plant strata from the vereda and to diagnose the recovery of native and invasive vegetation after prescribed burning. The study was carried out in the following five stages: biomass collection; quality analysis of the land cover; phytosociological survey; collection of control points using a GNSS receiver (type L1/L2); and the capture of aerial images with an RGB camera coupled to a DJI Phantom 4 Pro, which was performed through overflights in three different periods. Object–Based Image Analysis was subsequently performed using the Nearest Neighbor classifier combined with Feature Space Optimization, obtaining classifications with accuracy and Kappa indexes greater than 80% and 0.80, respectively. The results of image processing allowed us to infer that fire acted as a renewing agent for native vegetation and as an inhibiting agent for invasive vegetation. The classification analyses combined with the phytosociological analysis allowed us to infer that the vereda is in the process of maturation. Therefore, the study demonstrated the potential of data obtained by RPAS for the diagnosis and analysis of vegetation dynamics in small wetlands submitted to Integrated Fire Management (IFM). Full article
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28 pages, 13157 KiB  
Article
Mapping Key Indicators of Forest Restoration in the Amazon Using a Low-Cost Drone and Artificial Intelligence
by Rafael Walter Albuquerque, Daniel Luis Mascia Vieira, Manuel Eduardo Ferreira, Lucas Pedrosa Soares, Søren Ingvor Olsen, Luciana Spinelli Araujo, Luiz Eduardo Vicente, Julio Ricardo Caetano Tymus, Cintia Palheta Balieiro, Marcelo Hiromiti Matsumoto and Carlos Henrique Grohmann
Remote Sens. 2022, 14(4), 830; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14040830 - 10 Feb 2022
Cited by 10 | Viewed by 3964
Abstract
Monitoring the vegetation structure and species composition of forest restoration (FR) in the Brazilian Amazon is critical to ensuring its long-term benefits. Since remotely piloted aircrafts (RPAs) associated with deep learning (DL) are becoming powerful tools for vegetation monitoring, this study aims to [...] Read more.
Monitoring the vegetation structure and species composition of forest restoration (FR) in the Brazilian Amazon is critical to ensuring its long-term benefits. Since remotely piloted aircrafts (RPAs) associated with deep learning (DL) are becoming powerful tools for vegetation monitoring, this study aims to use DL to automatically map individual crowns of Vismia (low resilience recovery indicator), Cecropia (fast recovery indicator), and trees in general (this study refers to individual crowns of all trees regardless of species as All Trees). Since All Trees can be accurately mapped, this study also aims to propose a tree crown heterogeneity index (TCHI), which estimates species diversity based on: the heterogeneity attributes/parameters of the RPA image inside the All Trees results; and the Shannon index measured by traditional fieldwork. Regarding the DL methods, this work evaluated the accuracy of the detection of individual objects, the quality of the delineation outlines and the area distribution. Except for Vismia delineation (IoU = 0.2), DL results presented accurate values in general, as F1 and IoU were always greater than 0.7 and 0.55, respectively, while Cecropia presented the most accurate results: F1 = 0.85 and IoU = 0.77. Since All Trees results were accurate, the TCHI was obtained through regression analysis between the canopy height model (CHM) heterogeneity attributes and the field plot data. Although TCHI presented robust parameters, such as p-value < 0.05, its results are considered preliminary because more data are needed to include different FR situations. Thus, the results of this work show that low-cost RPA has great potential for monitoring FR quality in the Amazon, because Vismia, Cecropia, and All Trees can be automatically mapped. Moreover, the TCHI preliminary results showed high potential in estimating species diversity. Future studies must assess domain adaptation methods for the DL results and different FR situations to improve the TCHI range of action. Full article
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