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Advanced Earth Observations of Forest and Wetland Environment

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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 (20 November 2022) | Viewed by 36286

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Special Issue Editors

Prof. Dr. Siyuan Wang

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Guest Editor

Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
Interests: ecosystem modeling; remote sensing and ecological big data intelligent processing; climate change

Dr. Qianqian Zhang

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Guest Editor

Centre for Crop and Disease Management, Curtin University, Perth, WA 6845, Australia
Interests: high-performance computing; GIS analysis; ecosystem modeling; satellite image analysis
Special Issues, Collections and Topics in MDPI journals

Dr. Hao Jiang

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Guest Editor

Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
Interests: satellite image processing; deep learning; remote sensing; advanced machine learning

Dr. Cong Ou

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Guest Editor

Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: land use science; satellite image analysis; human geography; rural development
Special Issues, Collections and Topics in MDPI journals

Dr. Yu Feng

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Guest Editor

Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
Interests: ecological restoration; land use; ecosystem services; human-nature relationship; waiting website
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleague,

Water, wetlands, and forests are constantly interacting to produce healthy and productive ecosystems. Recent erratic spatial precipitation events are hard to predict, and thus, so is understanding vulnerability toward climate-associated environmental hazards, such as forest disease outbreak, forested wetland conversion to upland forests due to eroded soil deposition, tidal freshwater wetlands ecosystem decimation due to seawater intrusion, and many more. Earth observation plays an essential role in providing crucial information for integrated wetland and forests resource management and decision for large spatial extents quickly and effectively. The application of artificial intelligence to Earth observation is the best and highest-quality tool that can help to overcome the Earth’s most significant challenges.

In this Special Issue, we intend to share the most recent Earth observation technologies for monitoring wetland and forest environments.

Both original research articles and reviews are welcome. In particular, we solicit contributions describing:

Innovative methodologies for monitoring wetland and forests;
Hydroclimatic models and simulations driven by satellite data in wetlands and forests;
Applications of Earth observation techniques for monitoring water cycle and vegetation conditions;
Applications of remote sensing to wetland and forest resource management;
Remote sensing data assimilation within vegetation models;
Big data processing for large-scale relevant EO data analysis.

We look forward to receiving your contributions.

Prof. Dr. Siyuan Wang
Dr. Qianqian Zhang
Dr. Hao Jiang
Dr. Cong Ou
Dr. Yu Feng
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

Earth observation
Synthetic aperture radar
Wetland vegetation
Multi-sensors
Ecosystem function and service
Forest mapping
Image analysis applications to remote sensing
Big data processing

Published Papers (11 papers)

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Research

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27 pages, 9086 KiB

Open AccessArticle

Correcting Underestimation and Overestimation in PolInSAR Forest Canopy Height Estimation Using Microwave Penetration Depth

by Hongbin Luo, Cairong Yue, Ning Wang, Guangfei Luo and Si Chen

Remote Sens. 2022, 14(23), 6145; https://doi.org/10.3390/rs14236145 - 4 Dec 2022

Cited by 1 | Viewed by 1724

Abstract

PolInSAR is an active remote sensing technique that is widely used for forest canopy height estimation, with the random volume over ground (RVoG) model being the most classic and effective forest canopy height inversion approach. However, penetration of microwave energy into the forest often leads to a downward shift of the canopy phase center, which leads to model underestimation of the forest canopy height. In addition, in the case of sparse and low forests, the canopy height is overestimated, owing to the large ground-to-volume amplitude ratio in the RVoG model and severe temporal decorrelation effects. To solve this problem, in this study, we conducted an experiment on forest canopy height estimation with the RVoG model using L-band multi-baseline fully polarized PolInSAR data obtained from the Lope and Pongara test areas of the AfriSAR project. We also propose various RVoG model error correction methods based on penetration depth by analyzing the model’s causes of underestimation and overestimation. The results show that: (1) In tall forest areas, there is a general underestimation of canopy height, and the value of this underestimation correlates strongly with the penetration depth, whereas in low forest areas, there is an overestimation of canopy height owing to severe temporal decorrelation; in this instance, overestimation can also be corrected by the penetration depth. (2) Based on the reference height RH100, we used training sample iterations to determine the correction thresholds to correct low canopy overestimation and tall canopy underestimation; by applying these thresholds, the inversion error of the RVoG model can be improved to some extent. The corrected R² increased from 0.775 to 0.856, and the RMSE decreased from 7.748 m to 6.240 m in the Lope test area. (3) The results obtained using the infinite-depth volume condition p-value as the correction threshold were significantly better than the correction results for the reference height, with the corrected R² value increasing from 0.775 to 0.914 and the RMSE decreasing from 7.748 m to 4.796 m. (4) Because p-values require a true height input, we extended the application scale of the method by predicting p-values as correction thresholds via machine learning methods and polarized interference features; accordingly, the corrected R² increased from 0.775 to 0.845, and the RMSE decreased from 7.748 m to 6.422 m. The same pattern was obtained for the Pongara test area. Overall, the findings of this study strongly suggest that it is effective and feasible to use penetration depth to correct for RVoG model errors. Full article

(This article belongs to the Special Issue Advanced Earth Observations of Forest and Wetland Environment)

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17 pages, 2326 KiB

Open AccessArticle

Satellite Remote Sensing Identification of Discolored Standing Trees for Pine Wilt Disease Based on Semi-Supervised Deep Learning

by Jiahao Wang, Junhao Zhao, Hong Sun, Xiao Lu, Jixia Huang, Shaohua Wang and Guofei Fang

Remote Sens. 2022, 14(23), 5936; https://doi.org/10.3390/rs14235936 - 23 Nov 2022

Cited by 7 | Viewed by 1871

Abstract

Pine wilt disease (PWD) is the most dangerous biohazard of pine species and poses a serious threat to forest resources. Coupling satellite remote sensing technology and deep learning technology for the accurate monitoring of PWD is an important tool for the efficient prevention and control of PWD. We used Gaofen-2 remote sensing images to construct a dataset of discolored standing tree samples of PWD and selected three semantic segmentation models—DeepLabv3+, HRNet, and DANet—for training and to compare their performance. To build a GAN-based semi-supervised semantic segmentation model for semi-supervised learning training, the best model was chosen as the generator of generative adversarial networks (GANs). The model was then optimized for structural adjustment and hyperparameter adjustment. Aimed at the characteristics of Gaofen-2 images and discolored standing trees with PWD, this paper adopts three strategies—swelling prediction, raster vectorization, and forest floor mask extraction—to optimize the image identification process and results and conducts an application demonstration study in Nanping city, Fujian Province. The results show that among the three semantic segmentation models, HRNet was the optimal conventional semantic segmentation model for identifying discolored standing trees of PWD based on Gaofen-2 images and that its MIoU value was 68.36%. Additionally, the GAN-based semi-supervised semantic segmentation model GAN_HRNet_Semi improved the MIoU value by 3.10%, and its recognition segmentation accuracy was better than the traditional semantic segmentation model. The recall rate of PWD discolored standing tree monitoring in the demonstration area reached 80.09%. The combination of semi-supervised semantic segmentation technology and high-resolution satellite remote sensing technology provides new technical methods for the accurate wide-scale monitoring, prevention, and control of PWD. Full article

(This article belongs to the Special Issue Advanced Earth Observations of Forest and Wetland Environment)

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17 pages, 3849 KiB

Open AccessArticle

Vegetation Dynamics under Rapid Urbanization in the Guangdong–Hong Kong–Macao Greater Bay Area Urban Agglomeration during the Past Two Decades

by Shoubao Geng, Huamin Zhang, Fei Xie, Lanhui Li and Long Yang

Remote Sens. 2022, 14(16), 3993; https://doi.org/10.3390/rs14163993 - 16 Aug 2022

Cited by 9 | Viewed by 2244

Abstract

Detection of long-term vegetation dynamics is important for identifying vegetation improvement and degradation, especially for rapidly urbanizing regions with intensive land cover conversions. The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) urban agglomeration has experienced rapid urbanization during the past decades with profound impacts on vegetation, so there is an urgent need to evaluate vegetation dynamics across land use/cover change (LUCC). Based on the normalized difference vegetation index (NDVI) during 2001–2020, we used coefficient of variation, Theil–Sen median trend analysis, and Hurst exponent to analyze the spatiotemporal change and future consistency of vegetation growth among the main LUCC in the GBA. Results demonstrated that low NDVI values with high fluctuations were mainly distributed in the central urban areas, whereas high NDVI values with low fluctuations were primarily located in the peripheral hilly mountains. The area-averaged NDVI showed an overall increasing trend at a rate of 0.0030 year⁻¹, and areas with vegetation improvement (82.99%) were more than four times those with vegetation degradation (17.01%). The persistent forest and grassland and the regions converted from built-up to vegetation displayed the most obvious greening; NDVI in over 90% of these areas showed an increasing trend. In contrast, vegetation browning occurred in more than 60% of the regions converted from vegetation to built-up. Future vegetation change in most areas (91.37%) will continue the existing trends, and 80.06% of the GBA was predicted to develop in a benign direction, compared to 19.94% in a malignant direction. Our results contribute to in-depth understanding of vegetation dynamics during rapid urbanization in the GBA, which is crucial for vegetation conservation and land-use optimization. Full article

(This article belongs to the Special Issue Advanced Earth Observations of Forest and Wetland Environment)

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19 pages, 3613 KiB

Open AccessArticle

An Improved Submerged Mangrove Recognition Index-Based Method for Mapping Mangrove Forests by Removing the Disturbance of Tidal Dynamics and S. alterniflora

by Qing Xia, Ting-Ting He, Cheng-Zhi Qin, Xue-Min Xing and Wu Xiao

Remote Sens. 2022, 14(13), 3112; https://doi.org/10.3390/rs14133112 - 28 Jun 2022

Cited by 6 | Viewed by 2826

Abstract

Currently, it is a great challenge for remote sensing technology to accurately map mangrove forests owing to periodic inundation. A submerged mangrove recognition index (SMRI) using two high- and low-tide images was recently proposed to remove the influence of tides and identify mangrove forests. However, when the tidal height of the selected low-tide image is not at the lowest tidal level, the corresponding SMRI does not function well, which results in mangrove forests below the low tidal height being undetected. Furthermore, Spartina alterniflora Loisel (S. alterniflora) was introduced to China in 1979 and rapidly spread to become the most serious invasive plant along the Chinese coastline. The current SMRI has failed to distinguish S. alterniflora from submerged mangrove forests because of their similar spectral signatures. In this study, an SMRI-based mangrove forest mapping method was developed using the time series of Sentinel-2 images to mitigate the two aforementioned issues. In the proposed method, quantile synthesis was applied to the time series of Sentinel-2 images to generate a lowest-tide synthetic image for creating SMRI to identify submerged mangrove forests. Unsubmerged mangrove forests were classified using a support vector machine, and a preliminary mangrove forest map was created by merging them. In addition, S. alterniflora was distinguished from the mangrove forests by analyzing their phenological differences. Finally, mangrove forest mapping was performed by masking S. alterniflora. The proposed method was applied to the entire coastline of the Guangxi Province, China. The results showed that it can reliably and accurately identify submerged mangrove forests derived from SMRI by synthesizing low- and high-tide images using quantile synthesis, and the differentiation of S. alterniflora using phenological differences results in more accurate mangrove mapping. This work helps to improve the accuracy of mangrove forest mapping using SMRI and its feasibility for coastal wetland monitoring. It also provides data for sustainable management, ecological protection, and restoration of vegetation in coastal zones. Full article

(This article belongs to the Special Issue Advanced Earth Observations of Forest and Wetland Environment)

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24 pages, 7061 KiB

Open AccessArticle

Response of Vegetation Phenology to the Interaction of Temperature and Precipitation Changes in Qilian Mountains

by Cheng Li, Yuyang Zou, Jianfeng He, Wen Zhang, Lulu Gao and Dafang Zhuang

Remote Sens. 2022, 14(5), 1248; https://doi.org/10.3390/rs14051248 - 3 Mar 2022

Cited by 23 | Viewed by 2828

Abstract

Located at the junction between the continental climate region and marine climate region, the Qilian Mountains have experienced significant climate change. Vegetation phenology in the Qilian Mountains is sensitive to climate change. However, the response of vegetation phenology to temperature and precipitation change is still unclear, and the same is true for their interactions. First, we extracted grassland phenological parameters such as SOS (the start of the growing season), EOS (the end of the growing season), and LOS (the length of the growing season) from revised MODIS-NDVI data in the Qilian Mountains during the period from 2000 to 2019. Second, we analyzed change trends of the phenological parameters, temperature, and precipitation. Furthermore, the effects of each meteorological element changes and their interaction on multiple phenological parameters were detected using the GeoDetector method. The result implied that (1) the SOS in most areas except the northwestern mountain region showed an advanced trend (10 d/10a); the EOS showed a delayed trend in the southeast (5 d/10a), and an advanced trend (5 d/10a) in the northwest; the LOS showed an extended trend (10 d/10a) in the southeast, and a shortened trend (5 d/10a) in the northwest. (2) Compared with a single meteorological element in a single period, the interaction of temperature and precipitation in different periods had a higher impact on grassland phenology, with the maximum q-value increasing by about 0.4 for each phenological parameter. (3) The change in the grassland phenology in the Qilian Mountains was inconsistently complete with climate change in the spatial distribution. Our research reveals the response of grassland phenology to the interaction of different meteorological elements in different periods. Compared with a single element, this can reflect the response of vegetation phenology to climate change more comprehensively. Full article

(This article belongs to the Special Issue Advanced Earth Observations of Forest and Wetland Environment)

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18 pages, 1641 KiB

Open AccessArticle

Improved U-Net Remote Sensing Classification Algorithm Based on Multi-Feature Fusion Perception

by Chuan Yan, Xiangsuo Fan, Jinlong Fan and Nayi Wang

Remote Sens. 2022, 14(5), 1118; https://doi.org/10.3390/rs14051118 - 24 Feb 2022

Cited by 23 | Viewed by 8384

Abstract

The selection and representation of remote sensing image classification features play crucial roles in image classification accuracy. To effectively improve the classification accuracy of features, an improved U-Net network framework based on multi-feature fusion perception is proposed in this paper. This framework adds the channel attention module (CAM-UNet) to the original U-Net framework and cascades the shallow features with the deep semantic features, replaces the classification layer in the original U-Net network with a support vector machine, and finally uses the majority voting game theory algorithm to fuse the multifeature classification results and obtain the final classification results. This study used the forest distribution in Xingbin District, Laibin City, Guangxi Zhuang Autonomous Region as the research object, which is based on Landsat 8 multispectral remote sensing images, and, by combining spectral features, spatial features, and advanced semantic features, overcame the influence of the reduction in spatial resolution that occurs with the deepening of the network on the classification results. The experimental results showed that the improved algorithm can improve classification accuracy. Before the improvement, the overall segmentation accuracy and segmentation accuracy of the forestland increased from 90.50% to 92.82% and from 95.66% to 97.16%, respectively. The forest cover results obtained by the algorithm proposed in this paper can be used as input data for regional ecological models, which is conducive to the development of accurate and real-time vegetation growth change models. Full article

(This article belongs to the Special Issue Advanced Earth Observations of Forest and Wetland Environment)

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16 pages, 2034 KiB

Open AccessArticle

Accurate Identification of Pine Wood Nematode Disease with a Deep Convolution Neural Network

by Jixia Huang, Xiao Lu, Liyuan Chen, Hong Sun, Shaohua Wang and Guofei Fang

Remote Sens. 2022, 14(4), 913; https://doi.org/10.3390/rs14040913 - 14 Feb 2022

Cited by 16 | Viewed by 3063

Abstract

Pine wood nematode disease is a devastating pine disease that poses a great threat to forest ecosystems. The use of remote sensing methods can achieve macroscopic and dynamic detection of this disease; however, the efficiency and accuracy of traditional remote sensing image recognition methods are not always sufficient for disease detection. Deep convolutional neural networks (D-CNNs), a technology that has emerged in recent years, have an excellent ability to learn massive, high-dimensional image features and have been widely studied and applied in classification, recognition, and detection tasks involving remote sensing images. This paper uses Gaofen-1 (GF-1) and Gaofen-2 (GF-2) remote sensing images of areas with pine wood nematode disease to construct a D-CNN sample dataset, and we train five popular models (AlexNet, GoogLeNet, SqueezeNet, ResNet-18, and VGG16) through transfer learning. Finally, we use the “macroarchitecture combined with micromodules for joint tuning and improvement” strategy to improve the model structure. The results show that the transfer learning effect of SqueezeNet on the sample dataset is better than that of other popular models and that a batch size of 64 and a learning rate of 1 × 10⁻⁴ are suitable for SqueezeNet’s transfer learning on the sample dataset. The improvement of SqueezeNet’s fire module structure by referring to the Slim module structure can effectively improve the recognition efficiency of the model, and the accuracy can reach 94.90%. The final improved model can help users accurately and efficiently conduct remote sensing monitoring of pine wood nematode disease. Full article

(This article belongs to the Special Issue Advanced Earth Observations of Forest and Wetland Environment)

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25 pages, 6555 KiB

Open AccessArticle

Fine-Scale Mapping of Natural Ecological Communities Using Machine Learning Approaches

by Parth Bhatt, Ann Maclean, Yvette Dickinson and Chandan Kumar

Remote Sens. 2022, 14(3), 563; https://doi.org/10.3390/rs14030563 - 25 Jan 2022

Cited by 11 | Viewed by 4730

Abstract

Remote sensing technology has been used widely in mapping forest and wetland communities, primarily with moderate spatial resolution imagery and traditional classification techniques. The success of these mapping efforts varies widely. The natural communities of the Laurentian Mixed Forest are an important component of Upper Great Lakes ecosystems. Mapping and monitoring these communities using high spatial resolution imagery benefits resource management, conservation and restoration efforts. This study developed a robust classification approach to delineate natural habitat communities utilizing multispectral high-resolution (60 cm) National Agriculture Imagery Program (NAIP) imagery data. For accurate training set delineation, NAIP imagery, soils data and spectral enhancement techniques such as principal component analysis (PCA) and independent component analysis (ICA) were integrated. The study evaluated the importance of biogeophysical parameters such as topography, soil characteristics and gray level co-occurrence matrix (GLCM) textures, together with the normalized difference vegetation index (NDVI) and NAIP water index (WINAIP) spectral indices, using the joint mutual information maximization (JMIM) feature selection method and various machine learning algorithms (MLAs) to accurately map the natural habitat communities. Individual habitat community classification user’s accuracies (UA) ranged from 60 to 100%. An overall accuracy (OA) of 79.45% (kappa coefficient (k): 0.75) with random forest (RF) and an OA of 75.85% (k: 0.70) with support vector machine (SVM) were achieved. The analysis showed that the use of the biogeophysical ancillary data layers was critical to improve interclass separation and classification accuracy. Utilizing widely available free high-resolution NAIP imagery coupled with an integrated classification approach using MLAs, fine-scale natural habitat communities were successfully delineated in a spatially and spectrally complex Laurentian Mixed Forest environment. Full article

(This article belongs to the Special Issue Advanced Earth Observations of Forest and Wetland Environment)

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21 pages, 9381 KiB

Open AccessArticle

Wetland Vegetation Classification through Multi-Dimensional Feature Time Series Remote Sensing Images Using Mahalanobis Distance-Based Dynamic Time Warping

by Huayu Li, Jianhua Wan, Shanwei Liu, Hui Sheng and Mingming Xu

Remote Sens. 2022, 14(3), 501; https://doi.org/10.3390/rs14030501 - 21 Jan 2022

Cited by 8 | Viewed by 2416

Abstract

Efficient methodologies for vegetation-type mapping are significant for wetland’s management practices and monitoring. Nowadays, dynamic time warping (DTW) based on remote sensing time series has been successfully applied to vegetation classification. However, most of the previous related studies only focused on Normalized Difference Vegetation Index (NDVI) time series while ignoring multiple features in each period image. In order to further improve the accuracy of wetland vegetation classification, Mahalanobis Distance-based Dynamic Time Warping (MDDTW) using multi-dimensional feature time series was employed in this research. This method extends the traditional DTW algorithm based on single-dimensional features to multi-dimensional features and solves the problem of calculating similarity distance between multi-dimensional feature time series. Vegetation classification experiments were carried out in the Yellow River Delta (YRD). Compared with different classification methods, the results show that the K-Nearest Neighbors (KNN) algorithm based on MDDTW (KNN-MDDTW) has achieved better classification accuracy; the overall accuracy is more than 90%, and kappa is more than 0.9. Full article

(This article belongs to the Special Issue Advanced Earth Observations of Forest and Wetland Environment)

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19 pages, 10153 KiB

Open AccessArticle

Mapping Large-Scale Plateau Forest in Sanjiangyuan Using High-Resolution Satellite Imagery and Few-Shot Learning

by Zhihao Wei, Kebin Jia, Xiaowei Jia, Pengyu Liu, Ying Ma, Ting Chen and Guilian Feng

Remote Sens. 2022, 14(2), 388; https://doi.org/10.3390/rs14020388 - 14 Jan 2022

Cited by 4 | Viewed by 1689

Abstract

Monitoring the extent of plateau forests has drawn much attention from governments given the fact that the plateau forests play a key role in global carbon circulation. Despite the recent advances in the remote-sensing applications of satellite imagery over large regions, accurate mapping of plateau forest remains challenging due to limited ground truth information and high uncertainties in their spatial distribution. In this paper, we aim to generate a better segmentation map for plateau forests using high-resolution satellite imagery with limited ground-truth data. We present the first 2 m spatial resolution large-scale plateau forest dataset of Sanjiangyuan National Nature Reserve, including 38,708 plateau forest imagery samples and 1187 handmade accurate plateau forest ground truth masks. We then propose an few-shot learning method for mapping plateau forests. The proposed method is conducted in two stages, including unsupervised feature extraction by leveraging domain knowledge, and model fine-tuning using limited ground truth data. The proposed few-shot learning method reached an F1-score of 84.23%, and outperformed the state-of-the-art object segmentation methods. The result proves the proposed few-shot learning model could help large-scale plateau forest monitoring. The dataset proposed in this paper will soon be available online for the public. Full article

(This article belongs to the Special Issue Advanced Earth Observations of Forest and Wetland Environment)

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Review

Jump to: Research

20 pages, 1313 KiB

Open AccessReview

SAR-to-Optical Image Translation and Cloud Removal Based on Conditional Generative Adversarial Networks: Literature Survey, Taxonomy, Evaluation Indicators, Limits and Future Directions

by Quan Xiong, Guoqing Li, Xiaochuang Yao and Xiaodong Zhang

Remote Sens. 2023, 15(4), 1137; https://doi.org/10.3390/rs15041137 - 19 Feb 2023

Cited by 11 | Viewed by 2498

Abstract

Due to the limitation of optical images that their waves cannot penetrate clouds, such images always suffer from cloud contamination, which causes missing information and limitations for subsequent agricultural applications, among others. Synthetic aperture radar (SAR) is able to provide surface information for all times and all weather. Therefore, translating SAR or fusing SAR and optical images to obtain cloud-free optical-like images are ideal ways to solve the cloud contamination issue. In this paper, we investigate the existing literature and provides two kinds of taxonomies, one based on the type of input and the other on the method used. Meanwhile, in this paper, we analyze the advantages and disadvantages while using different data as input. In the last section, we discuss the limitations of these current methods and propose several possible directions for future studies in this field. Full article

(This article belongs to the Special Issue Advanced Earth Observations of Forest and Wetland Environment)

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