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Remote Sensing for Land Use and Vegetation Mapping
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Remote Sensing for Land Use and Vegetation Mapping

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: closed (31 August 2024) | Viewed by 19221

Special Issue Editors

Prof. Dr. Xiaobin Jin

E-Mail Website
Guest Editor
School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
Interests: land use; land cover; remote sensing; land consolidation; land system; landscape pattern; land sustainability; land ecology; cultivated land protection; food security
Prof. Dr. Giorgos Mallinis

E-Mail Website
Guest Editor
Laboratory of Photogrammetry and Remote Sensing (PERS Lab), School of Rural and Surveying Engineering, The Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece
Interests: land use/land cover (LULC) mapping; forests; classification development and comparison; geographic object-based image analysis; natural disasters; UAS; ecosystem services
Special Issues, Collections and Topics in MDPI journals
Dr. Bruce D. Chapman

E-Mail Website
Guest Editor
NASA Jet Propulsion Laboratory, M/S 300-149, Pasadena, CA 91109, USA
Interests: radar; SAR; land-cover/ land-use change; site conservation; archaeology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Long-term remote sensing monitoring of land use and vegetation on a global or regional scale is very important for realizing the sustainable development of national socioeconomics and the natural environment. Mapping land use patterns and tracking dynamics of vegetation change is essential for ecosystems gradient mapping from local to global scales as well as for earth surface process modeling through time.

Remote sensing has shown great potential to provide valuable information regarding the patterns, processes, mechanisms and management of land use and vegetation at various spatial and temporal scales. Recent evolutions in terms of high spatial and temporal data availability at low or no cost in the remote sensing community also facilitate information extraction on land use and vegetation with advanced computational methods including cloud processing and machine learning (shallow and deep learning) approaches.

With this Special Issue, we wish to compile state-of-the-art research that specifically addresses various aspects of the land use and vegetation remote sensing: national to global land use and vegetation monitoring, observations of vegetation phenology, spatial pattern and development trend of land use and vegetation, status and management of land use and vegetation, new remote sensing identification technology of land use and vegetation, vegetation distribution and climate change, land use and food security, land use change and urbanization, land use and sustainable development, etc. Contributions in the form of reviews are welcomed, as are papers describing new sensors for measurement.

Prof. Dr. Xiaobin Jin
Dr. Giorgos Mallinis
Dr. Bruce D. Chapman
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

  • land use and land cover
  • local, national & global land use patterns
  • local, national & global vegetation mapping
  • land use identification algorithms
  • land use and urbanization
  • vegetation and climate change
  • land use and food security
  • phenology
  • machine learning
  • cloud processing
  • time-series

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Published Papers (6 papers)

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18 pages, 5721 KiB  
Article
Detection of the Optimal Temporal Windows for Mapping Paddy Rice Under a Double-Cropping System Using Sentinel-2 Imagery
by Li Sheng, Yuefeng Lv, Zhouqiao Ren, Hongkui Zhou and Xunfei Deng
Remote Sens. 2025, 17(1), 57; https://doi.org/10.3390/rs17010057 - 27 Dec 2024
Viewed by 666
Abstract
Accurately mapping paddy rice is crucial for food security, sustainable agricultural management and environmental protection. Recently, Sentinel-2 optical images with a spatial resolution of 10 m and a repeat cycle of five days have demonstrated enormous potential for mapping paddy fields. However, the [...] Read more.
Accurately mapping paddy rice is crucial for food security, sustainable agricultural management and environmental protection. Recently, Sentinel-2 optical images with a spatial resolution of 10 m and a repeat cycle of five days have demonstrated enormous potential for mapping paddy fields. However, the influence of the temporal selection of Sentinel-2 optical images on mapping paddy rice is still unclear. In this study, the optimal temporal windows were detected by considering all possible temporal combinations during the growing stages from the constructed cloud-free 10-day time series and assessing the classification performances of all combination schemes on paddy rice mapping by F1_score. The results indicated that the combination of two or three phases is necessary for mapping early-cropping paddy rice (EP) and late-cropping paddy rice (LP), achieving the F1_score aim of 0.96. The detection of single-cropping paddy rice (SP) requires a combination of three to five phases and can obtain the F1_score aim of 0.94. Additionally, an automatic workflow for paddy rice mapping has been developed, which does not require any cloud removal but provides complete spatial coverage, suitable for regions with frequent rain and clouds. Through verification in the study area of Yiwu, China, the discrepancies between mapping results and agricultural statistics were within 5%, demonstrating the rationality and efficiency of the proposed framework. Full article
(This article belongs to the Special Issue Remote Sensing for Land Use and Vegetation Mapping)
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17 pages, 13358 KiB  
Article
Study on the Spatial and Temporal Distribution of Urban Vegetation Phenology by Local Climate Zone and Urban–Rural Gradient Approach
by Shan Li, Qiang Li, Jiahua Zhang, Shichao Zhang, Xue Wang, Shanshan Yang and Sha Zhang
Remote Sens. 2023, 15(16), 3957; https://doi.org/10.3390/rs15163957 - 10 Aug 2023
Cited by 3 | Viewed by 2196
Abstract
Understanding variations in the temporal and spatial distribution of vegetation phenology is essential for adapting to and mitigating future climate change and urbanization. However, there have been limited vegetation phenology studies within small-scale areas such as urban environments over the past decades. Therefore, [...] Read more.
Understanding variations in the temporal and spatial distribution of vegetation phenology is essential for adapting to and mitigating future climate change and urbanization. However, there have been limited vegetation phenology studies within small-scale areas such as urban environments over the past decades. Therefore, the present study focuses on Jinan city, Shandong Province, China as the study area and employs a more refined local climate zone (LCZ) approach to investigate spatial and temporal variations in vegetation phenology. The three phenological indicators used in this study from 2007 to 2018, namely, the start of growing season (SOS), the end of growing season (EOS), and the length of growing season (LOS), were provided by MODIS satellite data. The SOS, EOS, and LOS were superimposed on the LCZ and urban–rural gradient to analyze the changes in vegetation phenology, and the applicability of these two analysis methods in the study of urban vegetation phenology was compared by the honest significant difference test. We found that the SOS, EOS, and LOS of vegetation in the study area generally showed an advance, delay, and extension trend, respectively. The means of the SOS and EOS along different LCZ types varied noticeably more than those along urban–rural gradients. In 2016, 77.5%, 80.0%, and 75.8% of LCZ pairs indicated statistically significant differences for SOS, EOS, and LOS, respectively. This study provides a new perspective for the study of urban vegetation phenology which can help in management of urban-scale environments, identification of areas rich in biodiversity, and conservation and restoration of biodiversity in urban areas. Full article
(This article belongs to the Special Issue Remote Sensing for Land Use and Vegetation Mapping)
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13 pages, 5627 KiB  
Article
Variation in Vegetation Quality of Terrestrial Ecosystems in China: Coupling Analysis Based on Remote Sensing and Typical Stations Monitoring Data
by Luguang Jiang, Ye Liu and Haixia Xu
Remote Sens. 2023, 15(9), 2276; https://doi.org/10.3390/rs15092276 - 25 Apr 2023
Cited by 5 | Viewed by 1900
Abstract
Vegetation is the most important component of the terrestrial ecosystem. Scientific and quantitative analysis of changes in vegetation quality is of great significance to the realization of ecosystem sustainability. Based on data of remote sensing and typical station monitoring, we examined dynamic NDVI [...] Read more.
Vegetation is the most important component of the terrestrial ecosystem. Scientific and quantitative analysis of changes in vegetation quality is of great significance to the realization of ecosystem sustainability. Based on data of remote sensing and typical station monitoring, we examined dynamic NDVI (Normalized Difference Vegetation Index) changes in typical ecosystems from 1998 to 2020. We found that about 1/3 of China’s regions had significantly improved vegetation quality in the past 22 years, and 10% of the region had decreased, which indicated that China’s ecological situation is continuously improving. There is a large spatial heterogeneity in the trend of NDVI changes. The NDVI of agricultural and forest stations in the north of China rose relatively slowly. The NDVI of desert stations has a significant upward trend. The large-scale implementation of ecological restoration projects had improved vegetation conditions. The NDVI of forest stations and agricultural stations in the south of China still showed growth, which already has better vegetation conditions. This research can provide theoretical support for the long-term monitoring of different ecosystem types and ecological protection in China. Full article
(This article belongs to the Special Issue Remote Sensing for Land Use and Vegetation Mapping)
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25 pages, 23867 KiB  
Article
Early Crop Classification via Multi-Modal Satellite Data Fusion and Temporal Attention
by Frank Weilandt, Robert Behling, Romulo Goncalves, Arash Madadi, Lorenz Richter, Tiago Sanona, Daniel Spengler and Jona Welsch
Remote Sens. 2023, 15(3), 799; https://doi.org/10.3390/rs15030799 - 31 Jan 2023
Cited by 20 | Viewed by 7162
Abstract
In this article, we propose a deep learning-based algorithm for the classification of crop types from Sentinel-1 and Sentinel-2 time series data which is based on the celebrated transformer architecture. Crucially, we enable our algorithm to do early classification, i.e., predict crop types [...] Read more.
In this article, we propose a deep learning-based algorithm for the classification of crop types from Sentinel-1 and Sentinel-2 time series data which is based on the celebrated transformer architecture. Crucially, we enable our algorithm to do early classification, i.e., predict crop types at arbitrary time points early in the year with a single trained model (progressive intra-season classification). Such early season predictions are of practical relevance for instance for yield forecasts or the modeling of agricultural water balances, therefore being important for the public as well as the private sector. Furthermore, we improve the mechanism of combining different data sources for the prediction task, allowing for both optical and radar data as inputs (multi-modal data fusion) without the need for temporal interpolation. We can demonstrate the effectiveness of our approach on an extensive data set from three federal states of Germany reaching an average F1 score of 0.92 using data of a complete growing season to predict the eight most important crop types and an F1 score above 0.8 when doing early classification at least one month before harvest time. In carefully chosen experiments, we can show that our model generalizes well in time and space. Full article
(This article belongs to the Special Issue Remote Sensing for Land Use and Vegetation Mapping)
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19 pages, 5602 KiB  
Article
Mapping Paddy Rice in Complex Landscapes with Landsat Time Series Data and Superpixel-Based Deep Learning Method
by Hongguo Zhang, Binbin He and Jin Xing
Remote Sens. 2022, 14(15), 3721; https://doi.org/10.3390/rs14153721 - 3 Aug 2022
Cited by 12 | Viewed by 3095
Abstract
The spatial pattern and temporal variation in paddy rice areas captured by remote sensing imagery provide an effective way of performing crop management and developing suitable agricultural policies. However, fragmented and scattered rice paddies due to undulating and varied topography, and the availability [...] Read more.
The spatial pattern and temporal variation in paddy rice areas captured by remote sensing imagery provide an effective way of performing crop management and developing suitable agricultural policies. However, fragmented and scattered rice paddies due to undulating and varied topography, and the availability and quality of remote sensing images (e.g., frequent cloud coverage) pose significant challenges to accurate long-term rice mapping, especially for traditional pixel and phenological methods in subtropical monsoon regions. This study proposed a superpixel and deep-learning-based time series method to analyze Landsat time series data for paddy rice classification in complex landscape regions. First, a superpixel segmentation map was generated using a dynamic-time-warping-based simple non-iterative clustering algorithm with preprocessed spectral indices (SIs) time series data. Second, the SI images were overlaid onto the superpixel map to construct mean SIs time series for each superpixel. Third, a multivariate long short-term memory full convolution neural network (MLSTM-FCN) classifier was employed to learn time series features of rice paddies to produce accurate paddy rice maps. The method was evaluated using Landsat imagery from 2000 to 2020 in Cengong County, Guizhou Province, China. Results indicate that the superpixel MLSTM-FCN achieved a high performance with an overall accuracy varying from 0.9547 to 0.9721, which presents an 0.17–1.23% improvement compared to the random forest method. This study showed that combining spectral, spatial, and temporal features with deep learning methods can generate accurate paddy rice maps in complex landscape regions. Full article
(This article belongs to the Special Issue Remote Sensing for Land Use and Vegetation Mapping)
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Other

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17 pages, 7062 KiB  
Technical Note
Individual Tree Segmentation from Side-View LiDAR Point Clouds of Street Trees Using Shadow-Cut
by Zhouyang Hua, Sheng Xu and Yingan Liu
Remote Sens. 2022, 14(22), 5742; https://doi.org/10.3390/rs14225742 - 13 Nov 2022
Cited by 6 | Viewed by 2795
Abstract
Segmentation of vegetation LiDAR point clouds is an important method for obtaining individual tree structure parameters. The current individual tree segmentation methods are mainly for airborne LiDAR point clouds, which use elevation information to form a grid map for segmentation, or use canopy [...] Read more.
Segmentation of vegetation LiDAR point clouds is an important method for obtaining individual tree structure parameters. The current individual tree segmentation methods are mainly for airborne LiDAR point clouds, which use elevation information to form a grid map for segmentation, or use canopy vertices as seed points for clustering. Side-view LiDAR (vehicle LiDAR and hand-held LiDAR) can acquire more information about the lower layer of trees, but it is a challenge to perform the individual tree segmentation because the structure of side-view LiDAR point clouds is more complex. This paper proposes an individual tree segmentation method called Shadow-cut to extract the contours of the street tree point cloud. Firstly, we separated the region of the trees using the binary classifier (e.g., support vector machine) based on point cloud geometric features. Then, the optimal projection of the 3D point clouds to the 2D image is calculated and the optimal projection is the case where the pixels of the individual tree image overlap the least. Finally, after using the image segmentation algorithm to extract the tree edges in the 2D image, the corresponding 3D individual tree point cloud contours are matched with the pixels of individual tree edges in the 2D image. We conducted experiments with the proposed method on LiDAR data of urban street trees, and the correctness, completeness, and quality of the proposed individual tree segmentation method reached 91.67%, 85.33%, and 79.19%, which were superior to the CHM-based method by 2.70%, 6.19%, and 7.12%, respectively. The results show that this method is a practical and effective solution for individual tree segmentation in the LiDAR point clouds of street trees. Full article
(This article belongs to the Special Issue Remote Sensing for Land Use and Vegetation Mapping)
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