Special Issue "Remote Sensing Applications in Vegetation Classification"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 31 March 2022.

Special Issue Editors

Dr. Anna Jarocińska
E-Mail Website
Guest Editor
Department of Geoinformatics, Cartography and Remote Sensing, Chair of Geomatics and Information Systems, Faculty of Geography and Regional Studies, University of Warsaw, Poland, ul. Krakowskie Przedmieście 30, 00-927 Warsaw, Poland
Interests: hyperspectral imaging; vegetation classification; biophysical remote sensing; vegetation index; vegetation condition
Dr. Adriana Marcinkowska-Ochtyra
E-Mail Website
Guest Editor
Department of Geoinformatics, Cartography and Remote Sensing, Chair of Geomatics and Information Systems, Faculty of Geography and Regional Studies, University of Warsaw, ul. Krakowskie Przedmieście 30, 00-927 Warsaw, Poland
Interests: habitats; hyperspectral and multispectral imaging; mapping; multitemporal classification; species; vegetation monitoring; vegetation communities
Dr. Adrian Ochtyra
E-Mail Website
Guest Editor
Department of Geoinformatics, Cartography and Remote Sensing, Chair of Geomatics and Information Systems, Faculty of Geography and Regional Studies, University of Warsaw, ul. Krakowskie Przedmieście 30, 00-927 Warsaw, Poland
Interests: vegetation disturbance monitoring; multitemporal analysis; vegetation condition; forests; urban trees

Special Issue Information

Dear Colleagues,

Identification of vegetation and its species and communities is one of the most important issues in its study. One of the ideas of vegetation monitoring is the ability to identify species, communities, and habitats and remote sensing data allow obtaining such information remotely. Remote sensing has been utilized for vegetation inventories for many decades, using airborne and spaceborne remote sensing. At the same time, such techniques allow limiting field research, which is particularly important in protected areas with limited access, as well as inaccessible areas, such as mountains and wetlands. Remote sensing data also play a significant role in mapping species on urban areas, where, due to the legacy of species, it is most often impossible to identify them with the help of ground-based techniques. At the same time, the classification of vegetation is possible thanks to the constantly evolving classification algorithms, sensors, and the increasing possibilities of computer equipment.

Therefore, there is more and more research on the use of remote sensing techniques in this field. We would like to introduce a new Special Issue of Remote Sensing entitled “Remote Sensing Applications in Vegetation Classification”. We welcome submissions which provide the community with the most recent advancements on all aspects of vegetation classification, including but not limited to species, communities, and habitats on urban, agricultural, semi-natural, and natural areas. The Special Issue invites research papers describing the state of the art in the field of vegetation classification at national, continental, or global scales.

Dr. Anna Jarocińska
Dr. Adriana Marcinkowska-Ochtyra
Dr. Adrian Ochtyra
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 papers will be 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 2400 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

  • vegetation
  • multitemporal
  • classification
  • algorithm
  • species
  • vegetation communities
  • identification
  • mapping

Published Papers (3 papers)

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Research

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Article
Feature Fusion Approach for Temporal Land Use Mapping in Complex Agricultural Areas
Remote Sens. 2021, 13(13), 2517; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132517 - 27 Jun 2021
Cited by 1 | Viewed by 668
Abstract
Accurate temporal land use mapping provides important and timely information for decision making for large-scale management of land and crop production. At present, temporal land cover and crop classifications within a study area have neglected the differences between subregions. In this paper, we [...] Read more.
Accurate temporal land use mapping provides important and timely information for decision making for large-scale management of land and crop production. At present, temporal land cover and crop classifications within a study area have neglected the differences between subregions. In this paper, we propose a classification rule by integrating the terrain, time series characteristics, priority, and seasonality (TTPSR) with Sentinel-2 satellite imagery. Based on the time series of Normalized Difference Water Index (NDWI) and Vegetation Index (NDVI), a dynamic decision tree for forests, cultivation, urban, and water was created in Google Earth Engine (GEE) for each subregion to extract cultivated land. Then, with or without this cultivated land mask data, the original classification results for each subregion were completed based on composite image acquisition with five vegetation indices using Random Forest. During the post-reclassification process, a 4-bit coding rule based on terrain, type, seasonal rhythm, and priority was generated by analyzing the characteristics of the original results. Finally, statistical results and temporal mapping were processed. The results showed that feature importance was dominated by B2, NDWI, RENDVI, B11, and B12 over winter, and B11, B12, NDBI, B2, and B8A over summer. Meanwhile, the cultivated land mask improved the overall accuracy for multicategories (seven to eight and nine to 13 during winter and summer, respectively) in each subregion, with average ranges in the overall accuracy for winter and summer of 0.857–0.935 and 0.873–0.963, respectively, and kappa coefficients of 0.803–0.902 and 0.835–0.950, respectively. The analysis of the above results and the comparison with resampling plots identified various sources of error for classification accuracy, including spectral differences, degree of field fragmentation, and planting complexity. The results demonstrated the capability of the TTPSR rule in temporal land use mapping, especially with regard to complex crops classification and automated post-processing, thereby providing a viable option for large-scale land use mapping. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Vegetation Classification)
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Article
Fusion of GF and MODIS Data for Regional-Scale Grassland Community Classification with EVI2 Time-Series and Phenological Features
Remote Sens. 2021, 13(5), 835; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13050835 - 24 Feb 2021
Cited by 1 | Viewed by 815
Abstract
Satellite-borne multispectral data are suitable for regional-scale grassland community classification owing to comprehensive coverage. However, the spectral similarity of different communities makes it challenging to distinguish them based on a single multispectral data. To address this issue, we proposed a support vector machine [...] Read more.
Satellite-borne multispectral data are suitable for regional-scale grassland community classification owing to comprehensive coverage. However, the spectral similarity of different communities makes it challenging to distinguish them based on a single multispectral data. To address this issue, we proposed a support vector machine (SVM)–based method integrating multispectral data, two-band enhanced vegetation index (EVI2) time-series, and phenological features extracted from Chinese GaoFen (GF)-1/6 satellite with (16 m) spatial and (2 d) temporal resolution. To obtain cloud-free images, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) algorithm was employed in this study. By using the algorithm on the coarse cloudless images at the same or similar time as the fine images with cloud cover, the cloudless fine images were obtained, and the cloudless EVI2 time-series and phenological features were generated. The developed method was applied to identify grassland communities in Ordos, China. The results show that the Caragana pumila Pojark, Caragana davazamcii Sanchir and Salix schwerinii E. L. Wolf grassland, the Potaninia mongolica Maxim, Ammopiptanthus mongolicus S. H. Cheng and Tetraena mongolica Maxim grassland, the Caryopteris mongholica Bunge and Artemisia ordosica Krasch grassland, the Calligonum mongolicum Turcz grassland, and the Stipa breviflora Griseb and Stipa bungeana Trin grassland are distinguished with an overall accuracy of 87.25%. The results highlight that, compared to multispectral data only, the addition of EVI2 time-series and phenological features improves the classification accuracy by 9.63% and 14.7%, respectively, and even by 27.36% when these two features are combined together, and indicate the advantage of the fine images in this study, compared to 500 m moderate-resolution imaging spectroradiometer (MODIS) data, which are commonly used for grassland classification at regional scale, while using 16 m GF data suggests a 23.96% increase in classification accuracy with the same extracted features. This study indicates that the proposed method is suitable for regional-scale grassland community classification. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Vegetation Classification)
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Review

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Review
One-Class Classification of Natural Vegetation Using Remote Sensing: A Review
Remote Sens. 2021, 13(10), 1892; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13101892 - 12 May 2021
Cited by 1 | Viewed by 784
Abstract
Advances in remote sensing (RS) technology in recent years have increased the interest in including RS data into one-class classifiers (OCCs). However, this integration is complex given the interdisciplinary issues involved. In this context, this review highlights the advances and current challenges in [...] Read more.
Advances in remote sensing (RS) technology in recent years have increased the interest in including RS data into one-class classifiers (OCCs). However, this integration is complex given the interdisciplinary issues involved. In this context, this review highlights the advances and current challenges in integrating RS data into OCCs to map vegetation classes. A systematic review was performed for the period 2013–2020. A total of 136 articles were analyzed based on 11 topics and 30 attributes that address the ecological issues, properties of RS data, and the tools and parameters used to classify natural vegetation. The results highlight several advances in the use of RS data in OCCs: (i) mapping of potential and actual vegetation areas, (ii) long-term monitoring of vegetation classes, (iii) generation of multiple ecological variables, (iv) availability of open-source data, (v) reduction in plotting effort, and (vi) quantification of over-detection. Recommendations related to interdisciplinary issues were also suggested: (i) increasing the visibility and use of available RS variables, (ii) following good classification practices, (iii) bridging the gap between spatial resolution and site extent, and (iv) classifying plant communities. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Vegetation Classification)
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