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Image Analysis for Forest Environmental Monitoring

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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: 15 June 2024 | Viewed by 19600

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Dr. Alexandre Bernardino

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Instituto Superior Técnico, Institute for Systems and Robotics (ISR-Lisbon), 1049-001 Lisboa, Portugal
Interests: computer vision; cognitive science; control theory; machine learning; humanoid robotics; cognitive systems
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Dr. Alexandra Moutinho

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IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
Interests: nonlinear control; modelling and identification of dynamic systems; flight control; unmanned aerial vehicles; sensor fusion; intelligent sensors
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Dr. Catarina Barata

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Institute for Systems and Robotics, Instituto Superior Técnico, 1049-001 Lisboa, Portugal
Interests: machine learning; image analysis; computer vision
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Dr. El Khalil Cherif

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Institute for Systems and Robotics, Instituto Superior T écnico, University of Lisbon, 1649-004 Lisboa, Portugal
Interests: sustainability; coastal environment sciences; blue growth; environment sciences diplomacy; pollution; ocean sustainability; remote sensing and GIS

Special Issue Information

Dear Colleagues,

Forests are key resources for sustaining life on earth. They act as carbon sinks and are one of the most effective ways of fighting climate change. They are one of the most important sources of renewable energies in the form of wood fuel – currently as much as solar, hydroelectric and wind power combined. Forests cover about 30% of the total land area on earth and are the home of 80% of the planet’s terrestrial species (50% of the animals). They are, thus, one of the most valuable public assets on the planet that needs to be protected from many threats coming mostly from human activity: agriculture, wildfires, urbanization, unregulated timber extraction. Large-scale and mid-scale monitoring of forest environments can be done in cost-effective ways through remote sensing and airborne or land-based sensor analysis, automating many of the processes with current machine learning and pattern recognition methods. Higher quality sensors (higher resolution, spectral bands) and acquisition technologies are becoming increasingly available both for new earth observation satellites, terrestrial observation towers, and aerial vehicles (manned and unmanned). Both individually and in combination, these different observation methods can provide valuable data for resource management policies or first response action to abnormal events.

This Special Issue will accept papers on all aspects of the acquisition and analysis of aerial image (latu sensu, including RGB, hyperspectral, multispectral, LiDAR, Radar), and video acquired from airborne and/or spaceborne sensors, that have an impact in the monitoring of forest environments. Topics include, but are not limited to:

Classification, detection, and segmentation of vegetation cover.
Detection and tracking for monitoring animal life in forest environments.
Measurement of humidity, temperature, and biomass of vegetation cover.
Detection and segmentation of fire, smoke and burned area in wildfire events.
UAVs in the monitoring of forest environments.
Multispectral and hyperspectral image sensors and methods for forest analysis.
Geo-localization and mapping of events and landmarks in forest areas.
Data acquisition from airborne and spaceborne sensors.
Public datasets that contain aerial images/videos of forest environments.
Benchmarking of aerial image/video analysis methods in forest environments.
3D reconstruction of forest environment with airborne image, video, LiDAR, Radar.
Real-time data analysis for early detection and forecasting progression of wildfires.
Combining large-scale/high latency satellite data with low-scale/low-latency aerial data.

Dr. Alexandre Bernardino
Dr. Alexandra Moutinho
Dr. Catarina Barata
Dr. El Khalil Cherif
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

image analysis
remote sensing
environment monitoring
forests
multispectral and hyperspectral imaging
airborne image

Published Papers (8 papers)

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Research

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23 pages, 54206 KiB

Open AccessArticle

Tree Crown Segmentation and Diameter at Breast Height Prediction Based on BlendMask in Unmanned Aerial Vehicle Imagery

by Jie Xu, Minbin Su, Yuxuan Sun, Wenbin Pan, Hongchuan Cui, Shuo Jin, Li Zhang and Pei Wang

Remote Sens. 2024, 16(2), 368; https://doi.org/10.3390/rs16020368 - 16 Jan 2024

Viewed by 940

Abstract

The surveying of forestry resources has recently shifted toward precision and real-time monitoring. This study utilized the BlendMask algorithm for accurately outlining tree crowns and introduced a Bayesian neural network to create a model linking individual tree crown size with diameter at breast height (DBH). BlendMask accurately outlines tree crown shapes and contours, outperforming traditional watershed algorithms in segmentation accuracy while preserving edge details across different scales. Subsequently, the Bayesian neural network constructs a model predicting DBH from the measured crown area, providing essential data for managing forest resources and conducting biodiversity research. Evaluation metrics like precision rate, recall rate, F1-score, and mAP index comprehensively assess the method’s performance regarding tree density. BlendMask demonstrated higher accuracy at 0.893 compared to the traditional watershed algorithm’s 0.721 accuracy based on experimental results. Importantly, BlendMask effectively handles over-segmentation problems while preserving edge details across different scales. Moreover, adjusting parameters during execution allows for flexibility in achieving diverse image segmentation effects. This study addresses image segmentation challenges and builds a model linking crown area to DBH using the BlendMask algorithm and a Bayesian neural network. The average discrepancies between calculated and measured DBH for Ginkgo biloba, Pinus tabuliformis, and Populus nigra varitalica were 0.15 cm, 0.29 cm, and 0.49cm, respectively, all within the acceptable forestry error margin of 1 cm. BlendMask, besides its effectiveness in crown segmentation, proves useful for various vegetation classification tasks like broad-leaved forests, coniferous forests, and grasslands. With abundant training data and ongoing parameter adjustments, BlendMask attains improved classification accuracy. This new approach shows great potential for real-world use, offering crucial data for managing forest resources, biodiversity research, and related fields, aiding decision-making processes. Full article

(This article belongs to the Special Issue Image Analysis for Forest Environmental Monitoring)

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25 pages, 21236 KiB

Open AccessArticle

Improving the Accuracy of Random Forest Classifier for Identifying Burned Areas in the Tangier-Tetouan-Al Hoceima Region Using Google Earth Engine

by Houda Badda, El Khalil Cherif, Hakim Boulaassal, Miriam Wahbi, Otmane Yazidi Alaoui, Mustapha Maatouk, Alexandre Bernardino, Franco Coren and Omar El Kharki

Remote Sens. 2023, 15(17), 4226; https://doi.org/10.3390/rs15174226 - 28 Aug 2023

Cited by 1 | Viewed by 1797

Abstract

Forest fires have become a major concern in the northern parts of Morocco, particularly in the Tangier-Tetouan-Al Hoceima (TTA) region, causing significant damage to the environment and human lives. To address this pressing issue, this study proposes an approach that utilizes remote sensing (RS) and machine learning (ML) techniques to detect burned areas in the TTA region within the Google Earth Engine platform. The study focuses on burned areas resulting from forest fires in three specific locations in the TTA region that have experienced such fires in recent years, namely Tangier-Assilah in 2017, M’diq Fnideq in 2020, and Chefchaouen in 2021. In our study, we extensively explored multiple combinations of spectral indices, such as normalized burn ratio (dNBR), normalized difference vegetation index (dNDVI), soil-adjusted vegetation index (dSAVI), and burned area index (dBAI), in conjunction with Sentinel-2 (S2) satellite images. These combinations were employed within the Random Forest (RF) algorithm, allowing us to draw important conclusions. Initially, we assess the individual effectiveness of the dNBR index, which yields accuracy rates of 83%, 90%, and 82% for Tangier-Assilah, Chefchaouen, and M’diq Fnideq, respectively. Recognizing the need for improved outcomes, we expand our analysis by incorporating spectral indices and S2 bands. However, the results obtained from this expanded combination lack consistency and stability across different locations. While Tangier-Assilah and M’diq Fnideq experience accuracy improvements, reaching 95% and 88%, respectively, the inclusion of Sentinel bands has an adverse effect on Chefchaouen, resulting in a decreased accuracy of 87%. To achieve optimal accuracy, our focus shifted towards the combination of dNBR and the other spectral indices. The results were truly remarkable, with accuracy rates of 96%, 97%, and 97% achieved for Tangier-Assilah, Chefchaouen, and M’diq Fnideq, respectively. Our decision to prioritize the spectral indices was based on the feature importance method, which highlights the significance of each feature in the classification process. The practical implications of our study extend to fire management and prevention in the TTA region. The insights gained from our analysis can inform the development of effective policies and strategies to mitigate the impact of forest fires. By harnessing the potential of RS and ML techniques, along with the utilization of spectral indices, we pave the way for enhanced fire monitoring and response capabilities in the region. Full article

(This article belongs to the Special Issue Image Analysis for Forest Environmental Monitoring)

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24 pages, 16262 KiB

Open AccessArticle

Monitoring Forest Cover Dynamics Using Orthophotos and Satellite Imagery

by Lucian Blaga, Dorina Camelia Ilieș, Jan A. Wendt, Ioan Rus, Kai Zhu and Lóránt Dénes Dávid

Remote Sens. 2023, 15(12), 3168; https://doi.org/10.3390/rs15123168 - 18 Jun 2023

Cited by 2 | Viewed by 1772

Abstract

The assessment of changes in forest coverage is crucial for managing protected forest areas, particularly in the face of climate change. This study monitored forest cover dynamics in a 6535 ha mountain area located in north-west Romania as part of the Apuseni Natural Park from 2003 to 2019. Two approaches were used: vectorization from orthophotos and Google Earth images (in 2003, 2005, 2009, 2012, 2014, 2016, 2017, and 2019) and satellite imagery (Landsat 5 TM, 7 ETM, and 8 OLI) pre-processed to Surface Reflectance (SR) format from the same years. We employed four standard classifiers: Support Vector Machine (SVM), Random Forest (RF), Maximum Likelihood Classification (MLC), Spectral Angle Mapper (SAM), and three combined methods: Linear Spectral Unmixing (LSU) with Natural Breaks (NB), Otsu Method (OM) and SVM, to extract and classify forest areas. Our study had two objectives: 1) to accurately assess changes in forest cover over a 17-year period and 2) to determine the most efficient methods for extracting and classifying forest areas. We validated the results using performance metrics that quantify both thematic and spatial accuracy. Our results indicate a 9% loss of forest cover in the study area, representing 577 ha with an average decrease ratio of 33.9 ha/year⁻¹. Of all the methods used, SVM produced the best results (with an average score of 88% for Overall Quality (OQ)), followed by RF (with a mean value of 86% for OQ). Full article

(This article belongs to the Special Issue Image Analysis for Forest Environmental Monitoring)

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15 pages, 9427 KiB

Open AccessArticle

The Impacts of Quality-Oriented Dataset Labeling on Tree Cover Segmentation Using U-Net: A Case Study in WorldView-3 Imagery

by Tao Jiang, Maximilian Freudenberg, Christoph Kleinn, Alexander Ecker and Nils Nölke

Remote Sens. 2023, 15(6), 1691; https://doi.org/10.3390/rs15061691 - 21 Mar 2023

Cited by 1 | Viewed by 1601

Abstract

Deep learning has emerged as a prominent technique for extracting vegetation information from high-resolution satellite imagery. However, less attention has been paid to the quality of dataset labeling as compared to research into networks and models, despite data quality consistently having a high impact on final accuracies. In this work, we trained a U-Net model for tree cover segmentation in 30 cm WorldView-3 imagery and assessed the impact of training data quality on segmentation accuracy. We produced two reference tree cover masks of different qualities by labeling images accurately or roughly and trained the model on a combination of both, with varying proportions. Our results show that models trained with accurately delineated masks achieved higher accuracy (88.06%) than models trained on masks that were only roughly delineated (81.13%). When combining the accurately and roughly delineated masks at varying proportions, we found that the segmentation accuracy increased with the proportion of accurately delineated masks. Furthermore, we applied semisupervised active learning techniques to identify an efficient strategy for selecting images for labeling. This showed that semisupervised active learning saved nearly 50% of the labeling cost when applied to accurate masks, while maintaining high accuracy (88.07%). Our study suggests that accurate mask delineation and semisupervised active learning are essential for efficiently generating training datasets in the context of tree cover segmentation from high-resolution satellite imagery. Full article

(This article belongs to the Special Issue Image Analysis for Forest Environmental Monitoring)

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20 pages, 6378 KiB

Open AccessArticle

Large-Scale Populus euphratica Distribution Mapping Using Time-Series Sentinel-1/2 Data in Google Earth Engine

by Yan Peng, Guojin He, Guizhou Wang and Zhaoming Zhang

Remote Sens. 2023, 15(6), 1585; https://doi.org/10.3390/rs15061585 - 14 Mar 2023

Viewed by 1435

Abstract

Accurate and efficient large-scale mapping of P. euphratica distribution is of great importance for managing and protecting P. euphratica forests, policy making, and realizing sustainable development goals in the ecological environments of desert areas. In large regions, numerous types of vegetation exhibit spectral characteristics that closely resemble those of P. euphratica, such as Tamarix, artificial forests, and allée trees, posing challenges for the accurate identification of P. euphratica. To solve this issue, this paper presents a method for large-scale P. euphratica distribution mapping. The geographical distribution characteristics of P. euphratica were first utilized to rapidly locate the appropriate region of interest and to further reduce background complexity and interference from other similar objects. Spectral features, indices, phenological features, and backscattering features extracted from all the available Sentinel-2 MSI and Sentinel-1 SAR data from 2021 were regarded as the input for a random forest model used to classify P. euphratica in the GEE platform. The results were then compared with the results from the method using only spectral features and index features, the results from the method that only added phenological features, and the results from the method that added phenological features and backscattering features by visually and quantitatively referencing field-surveyed samples, UAV data, and high-spatial-resolution data from Google Earth Data and Map World. The comparison indicated that the proposed method, which adds both phenological and time-series backscattering features, could correctly distinguish P. euphratica from other types of vegetation that have spectral information similar to P. euphratica. The rates of omission errors (OEs), commission errors (CEs), and overall accuracy (OA) for the proposed method were 12.53%, 11.01%, and 89.32%, respectively, representing increases of approximately 9%, 17%, and 13% in comparison with the method using only spectral and index features. The proposed method significantly improved the accuracy of P. euphratica classification in terms of both omission and, especially, commission. Full article

(This article belongs to the Special Issue Image Analysis for Forest Environmental Monitoring)

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25 pages, 9282 KiB

Open AccessArticle

Deep Convolutional Compressed Sensing-Based Adaptive 3D Reconstruction of Sparse LiDAR Data: A Case Study for Forests

by Rajat C. Shinde and Surya S. Durbha

Remote Sens. 2023, 15(5), 1394; https://doi.org/10.3390/rs15051394 - 01 Mar 2023

Cited by 2 | Viewed by 1675

Abstract

LiDAR point clouds are characterized by high geometric and radiometric resolution and are therefore of great use for large-scale forest analysis. Although the analysis of 3D geometries and shapes has improved at different resolutions, processing large-scale 3D LiDAR point clouds is difficult due to their enormous volume. From the perspective of using LiDAR point clouds for forests, the challenge lies in learning local and global features, as the number of points in a typical 3D LiDAR point cloud is in the range of millions. In this research, we present a novel end-to-end deep learning framework called ADCoSNet, capable of adaptively reconstructing 3D LiDAR point clouds from a few sparse measurements. ADCoSNet uses empirical mode decomposition (EMD), a data-driven signal processing approach with Deep Learning, to decompose input signals into intrinsic mode functions (IMFs). These IMFs capture hierarchical implicit features in the form of decreasing spatial frequency. This research proposes using the last IMF (least varying component), also known as the Residual function, as a statistical prior for capturing local features, followed by fusing with the hierarchical convolutional features from the deep compressive sensing (CS) network. The central idea is that the Residue approximately represents the overall forest structure considering it is relatively homogenous due to the presence of vegetation. ADCoSNet utilizes this last IMF for generating sparse representation based on a set of CS measurement ratios. The research presents extensive experiments for reconstructing 3D LiDAR point clouds with high fidelity for various CS measurement ratios. Our approach achieves a maximum peak signal-to-noise ratio (PSNR) of 48.96 dB (approx. 8 dB better than reconstruction without data-dependent transforms) with reconstruction root mean square error (RMSE) of 7.21. It is envisaged that the proposed framework finds high potential as an end-to-end learning framework for generating adaptive and sparse representations to capture geometrical features for the 3D reconstruction of forests. Full article

(This article belongs to the Special Issue Image Analysis for Forest Environmental Monitoring)

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17 pages, 8772 KiB

Open AccessArticle

Future Scenarios of Land Use/Land Cover (LULC) Based on a CA-Markov Simulation Model: Case of a Mediterranean Watershed in Morocco

by Mohamed Beroho, Hamza Briak, El Khalil Cherif, Imane Boulahfa, Abdessalam Ouallali, Rachid Mrabet, Fassil Kebede, Alexandre Bernardino and Khadija Aboumaria

Remote Sens. 2023, 15(4), 1162; https://doi.org/10.3390/rs15041162 - 20 Feb 2023

Cited by 25 | Viewed by 3886

Abstract

Modeling of land use and land cover (LULC) is a very important tool, particularly in the agricultural field: it allows us to know the potential changes in land area in the future and to consider developments in order to prevent probable risks. The idea is to give a representation of probable future situations based on certain assumptions. The objective of this study is to make future predictions in land use and land cover in the watershed “9 April 1947”, and in the years 2028, 2038 and 2050. Then, the maps obtained with the climate predictions will be integrated into an agro-hydrological model to know the water yield, the sediment yield and the water balance of the studied area by 2050.The future land use and land cover (LULC) scenarios were created using a CA-Markov forecasting model. The results of the simulation of the LULC changes were considered satisfactory, as shown by the values obtained from the kappa indices for agreement (κstandard) = 0.73, kappa for lack of information (κno) = 0.76, and kappa for location at grid cell level (κlocation) = 0.80. Future scenarios modeled in LULC indicate a decrease in agricultural areas and wetlands, both of which can be seen as a warning of crop loss. There is, on the other hand, an increase in forest areas that could be an advantage for the biodiversity of the fauna and flora in the “9 April 1947” watershed. Full article

(This article belongs to the Special Issue Image Analysis for Forest Environmental Monitoring)

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Review

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40 pages, 8072 KiB

Open AccessReview

Latest Trends on Tree Classification and Segmentation Using UAV Data—A Review of Agroforestry Applications

by Babak Chehreh, Alexandra Moutinho and Carlos Viegas

Remote Sens. 2023, 15(9), 2263; https://doi.org/10.3390/rs15092263 - 25 Apr 2023

Cited by 9 | Viewed by 4445

Abstract

When it comes to forest management and protection, knowledge is key. Therefore, forest mapping is crucial to obtain the required knowledge towards profitable resource exploitation and increased resilience against wildfires. Within this context, this paper presents a literature review on tree classification and segmentation using data acquired by unmanned aerial vehicles, with special focus on the last decade (2013–2023). The latest research trends in this field are presented and analyzed in two main vectors, namely: (1) data, where used sensors and data structures are resumed; and (2) methods, where remote sensing and data analysis methods are described, with particular focus on machine learning approaches. The study and review methodology filtered 979 papers, which were then screened, resulting in the 144 works included in this paper. These are systematically analyzed and organized by year, keywords, purpose, sensors, and methods used, easily allowing the readers to have a wide, but at the same time detailed, view of the latest trends in automatic tree classification and segmentation using unmanned aerial vehicles. This review shows that image processing and machine learning techniques applied to forestry and segmentation and classification tasks are focused on improving the accuracy and interpretability of the results by using multi-modal data, 3D information, and AI methods. Most works use RGB or multispectral cameras, or LiDAR scanners, individually. Classification is mostly carried out using supervised methods, while segmentation mostly uses unsupervised machine learning techniques. Full article

(This article belongs to the Special Issue Image Analysis for Forest Environmental Monitoring)

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