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Remote Sensing
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Vegetation Classification and Mapping by Remote Sensing and Machine Learning
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Vegetation Classification and Mapping by Remote Sensing and Machine Learning

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

Deadline for manuscript submissions: closed (20 February 2024) | Viewed by 10875

Special Issue Editors

Prof. Dr. Kiwon Lee

E-Mail Website
Guest Editor
Department of Information Systems Engineering, Hansung University, Seoul 02876, Republic of Korea
Interests: land remote sensing; soft computing; data processing; ARD
Prof. Dr. No-Wook Park

E-Mail Website
Guest Editor
Department of Geoinformatic Engineering, Inha University, Incheon 22212, Republic of Korea
Interests: multi-sensor image fusion; remote sensing image classification; geo-AI
Special Issues, Collections and Topics in MDPI journals
Dr. Kwangseob Kim

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Hansung University, Seoul 02876, Republic of Korea
Interests: cloud computing; machine learning; remote sensing

Special Issue Information

Dear Colleague,

Vegetation classification and mapping by remote sensing and machine learning is a rapidly growing field that has gained significant attention recently. The use of machine learning algorithms in remote sensing has created high-resolution, accurate, and efficient maps of vegetation cover and types. Recent trends in vegetation classification and mapping by remote sensing and machine learning include the development of more advanced algorithms that can handle complex and heterogeneous landscapes. Additionally, there has been a focus on integrating multiple data sources, such as LiDAR and hyperspectral data, to improve the accuracy and increase the level of detail in vegetation maps.

Vegetation classification and mapping by remote sensing and machine learning have significant implications for understanding ecosystem dynamics, monitoring changes in vegetation cover and types, and informing land-use and conservation planning efforts. Accurate vegetation mapping can help understand climate change's impact on ecosystems and inform efforts to mitigate and adapt to these changes. Developing new algorithms and techniques for vegetation mapping using remote sensing and machine learning can help drive advances in remote sensing technologies, including developing new sensors and platforms. There has also been a push towards developing open-source software and data platforms to democratize access to remote sensing data and machine learning tools for vegetation mapping. This has led to the creation of large-scale global vegetation mapping initiatives.

This Special Issue invites the submission of studies covering vegetation classification and mapping by remote sensing and machine learning acquired by different sensors and platforms. Topics may cover anything from the application of a case study to modern technology within this theme. Articles may address, but are not limited, to the following topics:

  • Application of classic machine learning methodology to vegetation classification and mapping;
  • Modern machine learning methodology for feature extraction;
  • High-performance machine learning algorithms for vegetation mapping;
  • Accuracy assessment of machine learning in remote sensing;
  • Vegetation classification and mapping by remote sensing and machine learning using multi-sensors;
  • Regional/global scale programs for vegetation classification and mapping by machine learning.

Prof. Dr. Kiwon Lee
Prof. Dr. No-Wook Park
Dr. Kwangseob Kim
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 segmentation
  • feature extraction
  • high-performance machine learning algorithm for land application
  • vegetation classification by machine learning
  • land cover/use mapping by machine learning
  • accuracy assessment

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Published Papers (6 papers)

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Research

20 pages, 4564 KiB  
Article
Machine Learning Vegetation Filtering of Coastal Cliff and Bluff Point Clouds
by Phillipe Alan Wernette
Remote Sens. 2024, 16(12), 2169; https://doi.org/10.3390/rs16122169 - 15 Jun 2024
Viewed by 1234
Abstract
Coastal cliffs erode in response to short- and long-term environmental changes, but predicting these changes continues to be a challenge. In addition to a chronic lack of data on the cliff face, vegetation presence and growth can bias our erosion measurements and limit [...] Read more.
Coastal cliffs erode in response to short- and long-term environmental changes, but predicting these changes continues to be a challenge. In addition to a chronic lack of data on the cliff face, vegetation presence and growth can bias our erosion measurements and limit our ability to detect geomorphic erosion by obscuring the cliff face. This paper builds on past research segmenting vegetation in three-band red, green, blue (RGB) imagery and presents two approaches to segmenting and filtering vegetation from the bare cliff face in dense point clouds constructed from RGB images and structure-from-motion (SfM) software. Vegetation indices were computed from previously published research and their utility in segmenting vegetation from bare cliff face was compared against machine learning (ML) models for point cloud segmentation. Results demonstrate that, while existing vegetation indices and ML models are both capable of segmenting vegetation and bare cliff face sediments, ML models can be more efficient and robust across different growing seasons. ML model accuracy quickly reached an asymptote with only two layers and RGB images only (i.e., no vegetation indices), suggesting that these more parsimonious models may be more robust to a range of environmental conditions than existing vegetation indices which vary substantially from one growing season to another with changes in vegetation phenology. Full article
(This article belongs to the Special Issue Vegetation Classification and Mapping by Remote Sensing and Machine Learning)
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27 pages, 4028 KiB  
Article
Evaluation and Selection of Multi-Spectral Indices to Classify Vegetation Using Multivariate Functional Principal Component Analysis
by Simone Pesaresi, Adriano Mancini, Giacomo Quattrini and Simona Casavecchia
Remote Sens. 2024, 16(7), 1224; https://doi.org/10.3390/rs16071224 - 30 Mar 2024
Cited by 1 | Viewed by 1528
Abstract
The identification, classification and mapping of different plant communities and habitats is of fundamental importance for defining biodiversity monitoring and conservation strategies. Today, the availability of high temporal, spatial and spectral data from remote sensing platforms provides dense time series over different spectral [...] Read more.
The identification, classification and mapping of different plant communities and habitats is of fundamental importance for defining biodiversity monitoring and conservation strategies. Today, the availability of high temporal, spatial and spectral data from remote sensing platforms provides dense time series over different spectral bands. In the case of supervised mapping, time series based on classical vegetation indices (e.g., NDVI, GNDVI, …) are usually input characteristics, but the selection of the best index or set of indices (which guarantees the best performance) is still based on human experience and is also influenced by the study area. In this work, several different time series, based on Sentinel-2 images, were created exploring new combinations of bands that extend the classic basic formulas as the normalized difference index. Multivariate Functional Principal Component Analysis (MFPCA) was used to contemporarily decompose the multiple time series. The principal multivariate seasonal spectral variations identified (MFPCA scores) were classified by using a Random Forest (RF) model. The MFPCA and RF classifications were nested into a forward selection strategy to identify the proper and minimum set of indices’ (dense) time series that produced the most accurate supervised classification of plant communities and habitat. The results we obtained can be summarized as follows: (i) the selection of the best set of time series is specific to the study area and the habitats involved; (ii) well-known and widely used indices such as the NDVI are not selected as the indices with the best performance; instead, time series based on original indices (in terms of formula or combination of bands) or underused indices (such as those derivable with the visible bands) are selected; (iii) MFPCA efficiently reduces the dimensionality of the data (multiple dense time series) providing ecologically interpretable results representing an important tool for habitat modelling outperforming conventional approaches that consider only discrete time series. Full article
(This article belongs to the Special Issue Vegetation Classification and Mapping by Remote Sensing and Machine Learning)
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19 pages, 3099 KiB  
Article
Effect of Texture Feature Distribution on Agriculture Field Type Classification with Multitemporal UAV RGB Images
by Chun-Han Lee, Kuang-Yu Chen and Li-yu Daisy Liu
Remote Sens. 2024, 16(7), 1221; https://doi.org/10.3390/rs16071221 - 30 Mar 2024
Cited by 1 | Viewed by 2094
Abstract
Identifying farmland use has long been an important topic in large-scale agricultural production management. This study used multi-temporal visible RGB images taken from agricultural areas in Taiwan by UAV to build a model for classifying field types. We combined color and texture features [...] Read more.
Identifying farmland use has long been an important topic in large-scale agricultural production management. This study used multi-temporal visible RGB images taken from agricultural areas in Taiwan by UAV to build a model for classifying field types. We combined color and texture features to extract more information from RGB images. The vectorized gray-level co-occurrence matrix (GLCMv), instead of the common Haralick feature, was used as texture to improve the classification accuracy. To understand whether changes in the appearance of crops at different times affect image features and classification, this study designed a labeling method that combines image acquisition times and land use type to observe it. The Extreme Gradient Boosting (XGBoost) algorithm was chosen to build the classifier, and two classical algorithms, the Support Vector Machine and Classification and Regression Tree algorithms, were used for comparison. In the testing results, the highest overall accuracy reached 82%, and the best balance accuracy across categories reached 97%. In our comparison, the color feature provides the most information about the classification model and builds the most accurate classifier. If the color feature were used with the GLCMv, the accuracy would improve by about 3%. In contrast, the Haralick feature does not improve the accuracy, indicating that the GLCM itself contains more information that can be used to improve the prediction. It also shows that with combined image acquisition times in the label, the within-group sum of squares can be reduced by 2–31%, and the accuracy can be increased by 1–2% for some categories, showing that the change of crops over time was also an important factor of image features. Full article
(This article belongs to the Special Issue Vegetation Classification and Mapping by Remote Sensing and Machine Learning)
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22 pages, 19842 KiB  
Article
Extracting Shrubland in Deserts from Medium-Resolution Remote-Sensing Data at Large Scale
by Bo Zhong, Li Yang, Xiaobo Luo, Junjun Wu and Longfei Hu
Remote Sens. 2024, 16(2), 374; https://doi.org/10.3390/rs16020374 - 17 Jan 2024
Cited by 1 | Viewed by 1513
Abstract
Shrubs are important ecological barriers in desert regions and an important component of global carbon estimation. However, the shrubland in deserts has been hardly presented, although many high-quality land cover datasets with a 10 m scale based on remote-sensing data have been publicly [...] Read more.
Shrubs are important ecological barriers in desert regions and an important component of global carbon estimation. However, the shrubland in deserts has been hardly presented, although many high-quality land cover datasets with a 10 m scale based on remote-sensing data have been publicly released products. Therefore, the underestimation of carbon storage is inevitable with the absence of desert shrublands. The existing land-cover datasets have been analyzed and compared, and it has been found that the reason for missing the shrubland in deserts is mainly indued by the absence of shrubland samples, which are easy to neglect and difficult to retrieve. In this study, we developed a semi-automatic method to extract shrubland samples in deserts as the updated input for the machine-learning method. Firstly, the initial samples of desert shrublands were identified from the very high spatial-resolution (0.3~0.5 m) imagery on GEE, and the maximum NDVI from Sentinel-2 was used for double-checking. Secondly, a feature-based method was used to learn the feature from the initial samples and a similarity-based searching method was employed to automatically expand the samples. Finally, the expanded samples and their corresponding time-series satellite images were inputted into different machine-learning methods at a large region (1.63 × 106 km2) for extracting the shrubland in the desert. It was found that different combinations of feature variables and time-series combinations have different impacts on the overall accuracy (OA) of the classification results, as well as the performance of identifying and classifying the different land-cover types. Compared to the existing global-scale land-cover products, the proposed method can better identify the shrubland in deserts and show better overall accuracy. Full article
(This article belongs to the Special Issue Vegetation Classification and Mapping by Remote Sensing and Machine Learning)
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24 pages, 14033 KiB  
Article
Performance Assessment of Irrigation Projects in Nepal by Integrating Landsat Images and Local Data
by Adarsha Neupane and Yohei Sawada
Remote Sens. 2023, 15(18), 4633; https://doi.org/10.3390/rs15184633 - 21 Sep 2023
Viewed by 1944
Abstract
With growing global concern for food and water insecurity, an efficient method to monitor irrigation projects is essential, especially in the developing world where irrigation performance is often suboptimal. In Nepal, the irrigated area has not been objectively recorded, although their assessment has [...] Read more.
With growing global concern for food and water insecurity, an efficient method to monitor irrigation projects is essential, especially in the developing world where irrigation performance is often suboptimal. In Nepal, the irrigated area has not been objectively recorded, although their assessment has substantial implications for national policy, project’s annual budgets, and donor funding. Here, we present the application of Landsat images to measure irrigated areas in Nepal for the past 17 years to contribute to the assessment of the irrigation performance. Landsat 5 TM (2006–2011) and Landsat 8 OLI (2013–2022) images were used to develop a machine learning model, which classifies irrigated and non-irrigated areas in the study areas. The random forest classification achieved an overall accuracy of 82.2% and kappa statistics of 0.72. For the class of irrigation areas, the producer’s accuracy and consumer’s accuracy were 79% and 96%, respectively. Our regionally trained machine learning model outperforms the existing global cropland map, highlighting the need for such models for local irrigation project evaluations. We assess irrigation project performance and its drivers by combining long-term changes in satellite-derived irrigated areas with local data related to irrigation performance, such as annual budget, irrigation service fee, crop yield, precipitation, and main canal discharge. Full article
(This article belongs to the Special Issue Vegetation Classification and Mapping by Remote Sensing and Machine Learning)
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Graphical abstract

35 pages, 13732 KiB  
Article
Using Voting-Based Ensemble Classifiers to Map Invasive Phragmites australis
by Connor J. Anderson, Daniel Heins, Keith C. Pelletier and Joseph F. Knight
Remote Sens. 2023, 15(14), 3511; https://doi.org/10.3390/rs15143511 - 12 Jul 2023
Cited by 2 | Viewed by 1367
Abstract
Machine learning is frequently combined with imagery acquired from uncrewed aircraft systems (UASs) to detect invasive plants. Having prior knowledge of which machine learning algorithm will produce the most accurate results is difficult. This study examines the efficacy of a voting-based ensemble classifier [...] Read more.
Machine learning is frequently combined with imagery acquired from uncrewed aircraft systems (UASs) to detect invasive plants. Having prior knowledge of which machine learning algorithm will produce the most accurate results is difficult. This study examines the efficacy of a voting-based ensemble classifier to identify invasive Phragmites australis from three-band (red, green, blue; RGB) and five-band (red, green, blue, red edge, near-infrared; multispectral; MS) UAS imagery acquired over multiple Minnesota wetlands. A Random Forest, histogram-based gradient-boosting classification tree, and two artificial neural networks were used within the voting-based ensemble classifier. Classifications from the RGB and multispectral imagery were compared across validation sites both with and without post-processing from an object-based image analysis (OBIA) workflow (post-machine learning OBIA rule set; post-ML OBIA rule set). Results from this study suggest that a voting-based ensemble classifier can accurately identify invasive Phragmites australis from RGB and multispectral imagery. Accuracies greater than 80% were attained by the voting-based ensemble classifier for both the RGB and multispectral imagery. The highest accuracy, 91%, was achieved when using the multispectral imagery, a canopy height model, and a post-ML OBIA rule set. The study emphasizes the need for further research regarding the accurate identification of Phragmites australis at low stem densities. Full article
(This article belongs to the Special Issue Vegetation Classification and Mapping by Remote Sensing and Machine Learning)
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