Land Cover and Land Use Mapping Using Satellite Image

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Urban Contexts and Urban-Rural Interactions".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 8712

Special Issue Editor

Remote Sensing and Spatial Analysis Branch, Office of Research and Development, United States Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
Interests: mangrove forests mapping and monitoring using high resolution satellite data; global and continental land cover mapping and monitoring using multi-spectral, multi-temporal, and multi-platform remotely sensed data; image pre-processing, classification, and validation using cloud computing

Special Issue Information

Dear Colleagues,

Land cover is an important variable for many studies involving the Earth surface, such as climate, food security, hydrology, soil erosion, atmospheric quality, conservation biology, and plant functioning. Land cover of planet Earth is in constant flux due to both natural and anthropogenic forces. The regular and timely assessment and monitoring is needed to identify the rates, patterns, causes, and consequences of the change. Remote sensing has been widely recognized as the most economical and feasible approach to derive land cover information over large areas. With the advent of satellite technology, it is now possible to assess and monitor land use and land cover from local to global scales. Thanks also to the advancements in information technology, data science, and brainware, we can analyze satellite data in a timely manner. The free availability of satellite data, including landsat and sentinel data, is contributing to this endeavor. This journal issue will focus on how remote sensing data and information are being used for land use and land cover mapping and monitoring in various parts of the world. Selected papers might also be included in an edited book on land use and land cover.

Dr. Chandra Giri
Guest Editor

Manuscript Submission Information

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Keywords

  • land use

  • land cover
  • remote sensing
  • image processing
  • satellite data
  • mapping
  • monitoring
  • local
  • global

Published Papers (5 papers)

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Research

32 pages, 19690 KiB  
Article
Evaluating the Applicability of Global LULC Products and an Author-Generated Phenology-Based Map for Regional Analysis: A Case Study in Ecuador’s Ecoregions
by Gladys Maria Villegas Rugel, Daniel Ochoa, Jose Miguel Menendez and Frieke Van Coillie
Land 2023, 12(5), 1112; https://doi.org/10.3390/land12051112 - 22 May 2023
Viewed by 1422
Abstract
An accurate and detailed understanding of land-use change affected by anthropogenic actions is key to environmental policy decision-making and implementation. Although global land cover products have been widely used to monitor and analyse land use/land cover (LULC) change, the feasibility of using these [...] Read more.
An accurate and detailed understanding of land-use change affected by anthropogenic actions is key to environmental policy decision-making and implementation. Although global land cover products have been widely used to monitor and analyse land use/land cover (LULC) change, the feasibility of using these products at the regional level needs to be assessed due to the limitation and biases of generalised models from around the world. The main objective of the present study was to generate regional LULC maps of three target areas located in the main ecoregions of Ecuador at a resolution of 10 m using Google Earth Engine (GEE) cloud-based computing. Our approach is based on (1) Single Date Classification (SDC) that processes Sentinel-2 data into fuzzy rule-driven thematic classes, (2) rule refinement using Visible Infrared Imaging Radiometer Suite (VIIRS) data, and (3) phenology-based synthesis (PBS) classification that combines SDC into LULC based on the occurrence rule. Our results show that the three target areas were classified with an overall accuracy of over 80%. In addition, cross-comparison between the global land cover products and our LULC product was performed and we found discrepancies and inaccuracies in the global products due to the characteristics of the target areas that included a dynamic landscape. Our LULC product supplements existing official statistics and showcases the effectiveness of phenology-based mapping in managing land use by providing precise and timely data to support agricultural policies and ensure food security. Full article
(This article belongs to the Special Issue Land Cover and Land Use Mapping Using Satellite Image)
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20 pages, 11374 KiB  
Article
A Deep Feature Fusion Method for Complex Ground Object Classification in the Land Cover Ecosystem Using ZY1-02D and Sentinel-1A
by Shuai Li and Shufang Tian
Land 2023, 12(5), 1022; https://doi.org/10.3390/land12051022 - 06 May 2023
Viewed by 1230
Abstract
Despite the successful application of multimodal deep learning (MDL) methods for land use/land cover (LULC) classification tasks, their fusion capacity has not yet been substantially examined for hyperspectral and synthetic aperture radar (SAR) data. Hyperspectral and SAR data have recently been widely used [...] Read more.
Despite the successful application of multimodal deep learning (MDL) methods for land use/land cover (LULC) classification tasks, their fusion capacity has not yet been substantially examined for hyperspectral and synthetic aperture radar (SAR) data. Hyperspectral and SAR data have recently been widely used in land cover classification. However, the speckle noise of SAR and the heterogeneity with the imaging mechanism of hyperspectral data have hindered the application of MDL methods for integrating hyperspectral and SAR data. Accordingly, we proposed a deep feature fusion method called Refine-EndNet that combines a dynamic filter network (DFN), an attention mechanism (AM), and an encoder–decoder framework (EndNet). The proposed method is specifically designed for hyperspectral and SAR data and adopts an intra-group and inter-group feature fusion strategy. In intra-group feature fusion, the spectral information of hyperspectral data is integrated by fully connected neural networks in the feature dimension. The fusion filter generation network (FFGN) suppresses the presence of speckle noise and the influence of heterogeneity between multimodal data. In inter-group feature fusion, the fusion weight generation network (FWGN) further optimizes complementary information and improves fusion capacity. Experimental results from ZY1-02D satellite hyperspectral data and Sentinel-1A dual-polarimetric SAR data illustrate that the proposed method outperforms the conventional feature-level image fusion (FLIF) and MDL methods, such as S2ENet, FusAtNet, and EndNets, both visually and numerically. We first attempt to investigate the potentials of ZY1-02D satellite hyperspectral data affected by thick clouds, combined with SAR data for complex ground object classification in the land cover ecosystem. Full article
(This article belongs to the Special Issue Land Cover and Land Use Mapping Using Satellite Image)
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13 pages, 13350 KiB  
Article
The Extraction Method of Alfalfa (Medicago sativa L.) Mapping Using Different Remote Sensing Data Sources Based on Vegetation Growth Properties
by Ruifeng Wang, Fengling Shi and Dawei Xu
Land 2022, 11(11), 1996; https://doi.org/10.3390/land11111996 - 07 Nov 2022
Cited by 2 | Viewed by 1141
Abstract
Alfalfa (Medicago sativa L.) is one of the most widely planted forages due to its useful characteristics. Although alfalfa spatial distribution is an important source of basic data, manual surveys incur high survey costs, require large workloads and confront difficulties in collecting [...] Read more.
Alfalfa (Medicago sativa L.) is one of the most widely planted forages due to its useful characteristics. Although alfalfa spatial distribution is an important source of basic data, manual surveys incur high survey costs, require large workloads and confront difficulties in collecting data over large areas; remote sensing compensates for these shortcomings. In this study, the time-series variation characteristics of different vegetation types were analyzed, and the extraction method of alfalfa mapping was established according to different spatial- and temporal-resolution remote sensing data. The results provided the following conclusions: (1) when using the wave peak and valley number of normalized difference vegetation index (NDVI) curves, in the study area, the number of wave peak needed to be greater than 2 and the number of wave valley needed to be greater than 1; (2) 91.6% of alfalfa sampling points were extracted by moderate resolution imaging spectroradiometer (MODIS) data using the wave peak and valley method, and 5.0% of oats sampling points were extracted as alfalfa, while no other vegetation types met these conditions; (3) 85.3% of alfalfa sampling points were identified from Sentinel-2 multispectral instrument (MSI) data using the wave peak and valley method; 6.0% of grassland vegetation and 8.7% of oats satisfied the conditions, while other vegetation types did not satisfy this rule; and (4) the temporal phase selection was very important for alfalfa extraction using single-time phase remote sensing images; alfalfa was easily separated from other vegetation at the pre−wintering stage and was more difficult to separate at the spring regreening stage due to the variability in the alfalfa overwintering rate; the overall classification accuracy was 92.9% with the supervised classification method using support vector machine (SVM) at the pre-wintering stage. These findings provide a promising approach to alfalfa mapping using different remote sensing data. Full article
(This article belongs to the Special Issue Land Cover and Land Use Mapping Using Satellite Image)
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30 pages, 21072 KiB  
Article
A Joint Bayesian Optimization for the Classification of Fine Spatial Resolution Remotely Sensed Imagery Using Object-Based Convolutional Neural Networks
by Omer Saud Azeez, Helmi Z. M. Shafri, Aidi Hizami Alias and Nuzul Azam Haron
Land 2022, 11(11), 1905; https://doi.org/10.3390/land11111905 - 26 Oct 2022
Viewed by 1713
Abstract
In recent years, deep learning-based image classification has become widespread, especially in remote sensing applications, due to its automatic and strong feature extraction capability. However, as deep learning methods operate on rectangular-shaped image patches, they cannot accurately extract objects’ boundaries, especially in complex [...] Read more.
In recent years, deep learning-based image classification has become widespread, especially in remote sensing applications, due to its automatic and strong feature extraction capability. However, as deep learning methods operate on rectangular-shaped image patches, they cannot accurately extract objects’ boundaries, especially in complex urban settings. As a result, combining deep learning and object-based image analysis (OBIA) has become a new avenue in remote sensing studies. This paper presents a novel approach for combining convolutional neural networks (CNN) with OBIA based on joint optimization of segmentation parameters and deep feature extraction. A Bayesian technique was used to find the best parameters for the multiresolution segmentation (MRS) algorithm while the CNN model learns the image features at different layers, achieving joint optimization. The proposed classification model achieved the best accuracy, with 0.96 OA, 0.95 Kappa, and 0.96 mIoU in the training area and 0.97 OA, 0.96 Kappa, and 0.97 mIoU in the test area, outperforming several benchmark methods including Patch CNN, Center OCNN, Random OCNN, and Decision Fusion. The analysis of CNN variants within the proposed classification workflow showed that the HybridSN model achieved the best results compared to 2D and 3D CNNs. The 3D CNN layers and combining 3D and 2D CNN layers (HybridSN) yielded slightly better accuracies than the 2D CNN layers regarding geometric fidelity, object boundary extraction, and separation of adjacent objects. The Bayesian optimization could find comparable optimal MRS parameters for the training and test areas, with excellent quality measured by AFI (0.046, −0.037) and QR (0.945, 0.932). In the proposed model, higher accuracies could be obtained with larger patch sizes (e.g., 9 × 9 compared to 3 × 3). Moreover, the proposed model is computationally efficient, with the longest training being fewer than 25 s considering all the subprocesses and a single training epoch. As a result, the proposed model can be used for urban and environmental applications that rely on VHR satellite images and require information about land use. Full article
(This article belongs to the Special Issue Land Cover and Land Use Mapping Using Satellite Image)
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23 pages, 5833 KiB  
Article
Spatiotemporal Dynamics of Landscape Transformation in Western Balkans’ Metropolitan Areas
by Isra Hyka, Artan Hysa, Sokol Dervishi, Marijana Kapovic Solomun, Alban Kuriqi, Dinesh Kumar Vishwakarma and Paul Sestras
Land 2022, 11(11), 1892; https://doi.org/10.3390/land11111892 - 25 Oct 2022
Cited by 14 | Viewed by 2346
Abstract
Human-caused landscape transformation represents a danger to conserving the Earth’s natural habitats. Landscape fragmentation (LF) caused by transportation infrastructure and urban development poses a threat to human and environmental health by increasing traffic noise and pollution, reducing the size and viability of wildlife [...] Read more.
Human-caused landscape transformation represents a danger to conserving the Earth’s natural habitats. Landscape fragmentation (LF) caused by transportation infrastructure and urban development poses a threat to human and environmental health by increasing traffic noise and pollution, reducing the size and viability of wildlife populations, facilitating the spread of invasive species, and reducing the recreational qualities of the landscape. It is especially noticeable in the metropolitan areas of developing countries due to rapid and unsupervised urban sprawl. In this context, this study aims to protect natural landscapes and biodiversity, promoting forms of sustainable development. To exemplify our aim, we bring a spatio-temporal analysis of landscape change comparing three metropolitan areas in the Western Balkans (WB). First, we compare the land use land cover (LULC) changes in Tirana (Albania), Skopje (North Macedonia), and Sarajevo (Bosnia and Herzegovina). The comparison was based on the Urban Atlas (UA) data of 2012 and 2018. The analysis was performed on two levels, at the metropolitan and urban spatial scales. Apart from descriptive statistics about the changes in surface area and patch counts, we used effective mesh size (meff) as a landscape metric to quantify the LF level. Our results show that each city has faced significant LULC change between 2012 and 2018, with a dominant increase in artificial surfaces. Furthermore, the cumulative natural surface area reduction is followed by increased landscape patch counts, indicating an increased LF at both levels. This study enhances public awareness about the landscape transformation trends in the developing metropolitan regions of WB. The respective administrative bodies at both local and central levels are invited to consider our results and adopt proper measurements to reduce the adverse consequences of subsequent spatial development decisions. Full article
(This article belongs to the Special Issue Land Cover and Land Use Mapping Using Satellite Image)
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